Eksploracja danych
Guz Bartłomiej & Żak Cezary
2024-06-12
Projekt z eksploracji danych
Wstęp
Poniższa analiza dotyczy parametrów samochodów osobowych, wystawionych na sprzedaż za pośrednictwem portalu internetowego otomoto.pl z okolic Lublina (w promieniu 50 km).
Informacje o samochodach zostały pobrane ze strony za pomocą osobnego skryptu napisanego w R:
otomoto_scraping.R (Opis działania skryptu)
Czego możemy się dowiedzieć?
Analiza parametrów samochodów osobowych może obejmować wiele różnych czynników, w zależności od tego, jakie kryteria są ważne dla konkretnego klienta. Przykłady parametrów, które mogą być brane pod uwagę, to m.in. moc silnika, pojemność skokowa, rodzaj paliwa, wielkość i typ nadwozia, wyposażenie, przestronność wnętrza, bezpieczeństwo i wygodę. Parametry te mogą być również porównywane między różnymi modelami samochodów, aby uzyskać informacje na temat ich wydajności, jakości i kosztów eksploatacji. Analiza parametrów samochodów osobowych może być używana do określenia najlepszej oferty cenowej w stosunku do określonych wymagań klienta.
Opis Parametrów
Baza danych zawiera następujące parametry:
id - wybrany przez nas indywidualny numer pojazdu
Marka_pojazdu
Model_pojazdu
Rok_produkcji
Przebieg - liczba przejechanych kilometrów
Pojemność skokowa:
- Objętość (pojemność) skokową silnika – iloczyn
objętości (pojemności) skokowej cylindra i liczby cylindrów:\[
V=\frac{\pi}{4} \cdot d^{2} \cdot s \cdot n\]
gdzie:
\(d\) - średnica cylindra
\(s\) - skok tłoka
\(n\) - liczba cylindów
- Objętość (pojemność) skokową silnika – iloczyn
objętości (pojemności) skokowej cylindra i liczby cylindrów:\[
V=\frac{\pi}{4} \cdot d^{2} \cdot s \cdot n\]
Rodzaj_paliwa:
Samochody osobowe mogą pracować na różnych rodzajach paliw. Najbardziej popularnyme dla samochodów osobowych to benzyna, diesel, gaz ziemny, napęd hybrydowy i energia elektryczna.
Benzyna jest paliwem zawierającym zwykle około 85% węglowodorów i jest używana do napędzania większości samochodów osobowych. Jest to jedno z najtańszych paliw dostępnych na rynku, a do tego jest łatwo dostępne. Jednakże jest ona również bardzo szkodliwa dla środowiska, ponieważ wytwarza duże ilości dwutlenku węgla, tlenków azotu i innych zanieczyszczeń.
Diesel to aktualnie najpopularniejszy rdzaj paliw stosowanych w samochodach osobowych. Jest ono wysoce wydajne i ekonomiczne, dlatego zyskuje popularność wśród tych, którzy szukają oszczędności. Jednak w porównaniu z benzyną, diesel wytwarza więcej tlenków azotu i innych zanieczyszczeń, które są szkodliwe dla środowiska.
Gaz ziemny (LPG) jest jednym z najbardziej ekologicznych paliw stosowanych w samochodach osobowych. Wytwarzany z naturalnych i odnawialnych źródeł, takich jak metan i węgiel. Jego wytwarzanie wytwarza mniej dwutlenku węgla i innych zanieczyszczeń niż benzyna lub diesel.
Napęd hybrydowy jest połączeniem benzyny i energii elektrycznej. Samochody hybrydowe wykorzystują silnik spalinowy do napędzania samochodu, ale również wykorzystują silnik elektryczny, aby wspierać pracę silnika spalinowego i zwiększyć wydajność.
Energia elektryczna jest coraz popularniejszym paliwem w samochodach osobowych. Samochody elektryczne nie wytwarzają żadnych szkodliwych zanieczyszczeń, co czyni je znacznie przyjazniejszymi dla środowiska. Jednak samochody elektryczne są zazwyczaj droższe od samochodów na benzynę lub diesel, a także mają ograniczone zasięgi.
Moc :
Parametr wyrażony w koniach mechanicznych (KM), oznaczający pracę, jaką jest w stanie wykonać silnik w określonym czasie. Im wyższa jest moc, tym krótszy będzie czas potrzebny do np. rozpędzenia auta. Maksymalna moc wynika z momentu obrotowego oraz prędkości obrotowej silnika i również jest osiągana w określonych warunkach, przeważnie w górnej skali obrotów.
Typ_nadwozia:
Typy nadwozia samochodów osobowych obejmują:
Sedan: Najbardziej popularny typ samochodu osobowego, charakteryzujący się czterema drzwiami i kontuarem.
Kombi: Jest to samochód osobowy ze względu na swoją elastyczność i przestronność. Zazwyczaj ma cztery drzwi, kontur i szerokie tylne siedzenia.
Hatchback(auta miejskie): Samochód osobowy z drzwiami tylnymi i konturem. Jest to wygodny wybór dla osób, które często transportują duże ilości bagażu.
Coupe: składa się z dwóch lub trzech drzwi z zazwyczaj zmniejszoną liczbą siedzeń w porównaniu z tradycyjnym sedanem. Charakteryzuje się długim, płaskim dachem i zazwyczaj zwężonym tyłem. Coupe zazwyczaj występuje w wersji sportowej i oferuje wyższą wydajność i więcej mocy niż sedan.
Kabriolet: Samochód osobowy z dachem składanym. Jest to wybór idealny dla osób, które chcą cieszyć się jazdą na otwartym powietrzu.
SUV: Składa się z czterech drzwi, dwóch lub trzech rzędów siedzeń, a także zazwyczaj znacznie większego przestrzeni bagażowej niż w zwykłym sedanie. SUV-y często mają wyższy prześwit niż sedan, co pozwala im na wjazd w tereny trudniej dostępne. SUV-y są zazwyczaj bardziej wytrzymałe i mają większą moc niż zwykłe samochody osobowe.
Minivan: Samochód osobowy z dużą przestrzenią w kabinie, który jest idealny dla rodzin z dziećmi.
Kompakt: Mały samochód osobowy z dużym bagażnikiem, który jest wyposażony w cztery drzwi i kontur. Jest to idealny wybór dla osób, które szukają oszczędności paliwa i mniejszych rozmiarów.
Auta małe: Bardzo mały samochód osobowy, który jest wyposażony w dwie lub cztery drzwi i kontur. Jest to wybór idealny dla osób, które szukają oszczędności paliwa i mniejszych rozmiarów.
Kolor
Kolor samochodu może być jednym z ważniejszych wyznaczników jego wyglądu. Odcień lakieru może wpływać na to, jak samochód jest postrzegany przez innych. Niektórzy uważają, że kolor samochodu może nawet wpływać na to, jak czują się za jego kierownicą.
Kolory samochodów są często związane z określonymi osobowościami. Na przykład, czerwony jest postrzegany jako kolor ekscytujący i dynamiczny, a czarny jest postrzegany jako kolor luksusowy i elegancki.
Cena
1. Czyszczenie Danych
Wczytanie danych
library(rio)
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.4.4 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ purrr::%||%() masks base::%||%()
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(knitr)
library(ggcorrplot)
autka <- import(file="otomoto.csv")
head(autka)|>
kable()| id | Marka_pojazdu | Model_pojazdu | Rok_produkcji | Przebieg | Pojemność_skokowa | Rodzaj_paliwa | Moc | Typ_nadwozia | Kolor | Cena |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Volkswagen | Polo | 2008 | 181 000 km | 1 390 cm3 | Benzyna | 80 KM | Auta miejskie | Czarny | 15999 |
| 2 | Kia | Ceed | 2008 | 179 000 km | 1 582 cm3 | Diesel | 115 KM | Kombi | Szary | 13500 |
| 3 | Toyota | Land Cruiser | 2022 | 2 000 km | 2 755 cm3 | Diesel | 204 KM | SUV | Beżowy | 287000 |
| 4 | Fiat | 500X | 2015 | 89 024 km | 1 368 cm3 | Benzyna | 140 KM | Auta miejskie | Inny kolor | 62730 |
| 5 | Toyota | Prius | 2009 | 289 820 km | 1 798 cm3 | Hybryda | 99 KM | Kompakt | Szary | 40500 |
| 6 | Ford | S-Max | 2007 | 345 000 km | 1 753 cm3 | Diesel | 125 KM | Minivan | Srebrny | 17600 |
Poszukiwanie brakujących danych
Jeżeli w procesie pobierania danych ze strony, jakiś parametr nie został wczytany, wtedy w jego miejsce zostaje zapisana wartość następnego parametru. Oznacza to, że w przypadku pojawienia się braków, ostatnia kolumna (Cena) będzie przyjmować wartość “NA”.
autka[is.na(autka$Cena)==T,] |>
kable()| id | Marka_pojazdu | Model_pojazdu | Rok_produkcji | Przebieg | Pojemność_skokowa | Rodzaj_paliwa | Moc | Typ_nadwozia | Kolor | Cena | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 585 | 585 | Renault | Zoe | 2017 | 60 000 km | Elektryczny | 40 KM | Kompakt | Biały | 21900 | NA |
| 1000 | 1000 | Mercedes-Benz | EQC | 2022 | Elektryczny | 408 KM | SUV | Szary | 391288 | NA | NA |
| 1001 | 1001 | Mercedes-Benz | EQS | 2022 | Elektryczny | 523 KM | Kompakt | Czarny | 783564 | NA | NA |
Braki pojawiają się dla samochodów o indeksach: 585, 1000, oraz 1001. Uwagę przykuwa fakt, że są to wyłącznie samochody elektryczne. Sprawdzmy więc dodatkowo, czy to wszystkie samochody elektryczne z bazy.
elektryczne <- NULL
for(i in 1:dim(autka)[1]){
for(j in 1:dim(autka)[2]){
if(is.na(autka[i,j])==F) {
if(autka[i,j]=="Elektryczny") {
elektryczne <- append(elektryczne,i)
}
}
}
}
autka[elektryczne,] |>
kable()| id | Marka_pojazdu | Model_pojazdu | Rok_produkcji | Przebieg | Pojemność_skokowa | Rodzaj_paliwa | Moc | Typ_nadwozia | Kolor | Cena | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 585 | 585 | Renault | Zoe | 2017 | 60 000 km | Elektryczny | 40 KM | Kompakt | Biały | 21900 | NA |
| 1000 | 1000 | Mercedes-Benz | EQC | 2022 | Elektryczny | 408 KM | SUV | Szary | 391288 | NA | NA |
| 1001 | 1001 | Mercedes-Benz | EQS | 2022 | Elektryczny | 523 KM | Kompakt | Czarny | 783564 | NA | NA |
Zgadza się. Tylko samochody elektryczne generują problemy z wczytywaniem danych, ponieważ nie posiadają takiego parametru jak Pojemość skokowa (przypiszmy im więc wartość 0). Dodatkowo, dla samochodów o indeksach 1000 oraz 1001 nie został wczytany parametr przebieg, może to być spowodowane tym, że są to nowe samochody, które nie przejechały jeszcze żadnej trasy. W tym przypadku również przypisujemy 0. Rodzaj paliwa uzupełniamy jako Elektryczny, a pozostałe wartości parametrów umieszczamy we właściwych kolumnach.
autka[elektryczne[1],7:11] <- autka[elektryczne[1],6:10]
autka[elektryczne[2:3],7:11] <- autka[elektryczne[2:3],5:9]
autka[elektryczne[2:3],5] <- 0
autka[elektryczne[1:3],6] <- 0
autka[elektryczne,] |>
kable()| id | Marka_pojazdu | Model_pojazdu | Rok_produkcji | Przebieg | Pojemność_skokowa | Rodzaj_paliwa | Moc | Typ_nadwozia | Kolor | Cena | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 585 | 585 | Renault | Zoe | 2017 | 60 000 km | 0 | Elektryczny | 40 KM | Kompakt | Biały | 21900 |
| 1000 | 1000 | Mercedes-Benz | EQC | 2022 | 0 | 0 | Elektryczny | 408 KM | SUV | Szary | 391288 |
| 1001 | 1001 | Mercedes-Benz | EQS | 2022 | 0 | 0 | Elektryczny | 523 KM | Kompakt | Czarny | 783564 |
Typy danych
Tworzymy ramkę danych z poprawionej bazy danych:
autka_dt <- data.frame(autka)colSums(is.na(autka_dt))## id Marka_pojazdu Model_pojazdu Rok_produkcji
## 0 0 0 0
## Przebieg Pojemność_skokowa Rodzaj_paliwa Moc
## 0 0 0 0
## Typ_nadwozia Kolor Cena
## 0 0 0Duplikaty
Pozbądźmy się na wstępie ewentualnych duplikatów
autka_dt <- autka_dt[!duplicated(autka_dt),]summary(autka_dt)## id Marka_pojazdu Model_pojazdu Rok_produkcji
## Min. : 1.0 Length:1248 Length:1248 Min. :1961
## 1st Qu.: 312.8 Class :character Class :character 1st Qu.:2007
## Median : 624.5 Mode :character Mode :character Median :2012
## Mean : 624.5 Mean :2011
## 3rd Qu.: 936.2 3rd Qu.:2016
## Max. :1248.0 Max. :2023
## Przebieg Pojemność_skokowa Rodzaj_paliwa Moc
## Length:1248 Length:1248 Length:1248 Length:1248
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Typ_nadwozia Kolor Cena
## Length:1248 Length:1248 Length:1248
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
## Parametry: Marka_pojazdu, Model pojazdu, Rodzaj, Paliwa, Typ_nadwozia oraz Kolor ustawiamy na typ kategoryczny (factor).
autka_dt$Marka_pojazdu <- as.factor(autka$Marka_pojazdu)
autka_dt$Model_pojazdu <- as.factor(autka$Model_pojazdu)
autka_dt$Kolor <- as.factor(autka$Kolor)
autka_dt$Typ_nadwozia <- as.factor(autka$Typ_nadwozia)
autka_dt$Rodzaj_paliwa <- as.factor(autka$Rodzaj_paliwa)Parametry takie jak: Przebieg,Pojemnosc,skokowa,Moc oraz Cena ustawiamy jako typ ilościowy (numeric).
autka_dt$Przebieg <- parse_number(gsub(" ","",autka_dt$Przebieg))
autka_dt$Pojemność_skokowa<- parse_number(gsub(" ","",autka_dt$Pojemność_skokowa))
autka_dt$Moc <- parse_number(gsub(" ","",autka_dt$Moc))
autka_dt$Cena <- parse_number(gsub(" ","",autka_dt$Cena))
summary(autka_dt)## id Marka_pojazdu Model_pojazdu Rok_produkcji
## Min. : 1.0 BMW :108 A4 : 30 Min. :1961
## 1st Qu.: 312.8 Audi : 96 Seria 3: 29 1st Qu.:2007
## Median : 624.5 Volvo : 85 Seria 5: 24 Median :2012
## Mean : 624.5 Volkswagen: 81 Passat : 22 Mean :2011
## 3rd Qu.: 936.2 Opel : 80 Focus : 21 3rd Qu.:2016
## Max. :1248.0 Renault : 80 Megane : 20 Max. :2023
## (Other) :718 (Other):1102
## Przebieg Pojemność_skokowa Rodzaj_paliwa Moc
## Min. : 0 Min. : 0 Benzyna :572 Min. : 30.0
## 1st Qu.: 102020 1st Qu.:1560 Benzyna+LPG: 97 1st Qu.:110.0
## Median : 176000 Median :1896 Diesel :544 Median :136.0
## Mean : 172701 Mean :1933 Elektryczny: 3 Mean :153.9
## 3rd Qu.: 237350 3rd Qu.:1997 Hybryda : 32 3rd Qu.:174.2
## Max. :2230000 Max. :6208 Max. :612.0
##
## Typ_nadwozia Kolor Cena
## SUV :312 Czarny :301 Min. : 1999
## Kombi :243 Szary :203 1st Qu.: 19700
## Sedan :200 Srebrny :181 Median : 36900
## Kompakt :193 Biały :169 Mean : 59409
## Minivan :125 Niebieski: 86 3rd Qu.: 63346
## Auta miejskie:110 Granatowy: 54 Max. :1626016
## (Other) : 65 (Other) :2542. Integracja Danych
Podstawowe statystyki opisowe
Przyjżyjmy się teraz podstawowym statystykom opisowym poszczególnych zmiennych.
Zmienne kotegoryczne
Marka pojazdu
autka_Marki <- autka_dt|>
count(Marka_pojazdu) |>
data.frame()
colnames(autka_Marki)[2] <- "Liczba_aut"
#posortowane malejąco względem liczby aut
autka_Marki_sort <- autka_Marki[order(autka_Marki$Liczba_aut, decreasing=T), ]
# %
autka_Marki_sort_procent <- round((autka_Marki_sort$Liczba_aut*100)/dim(autka_dt)[1],2)
cbind(autka_Marki_sort,autka_Marki_sort_procent)|>
kable(caption = "Liczba aut poszczególnych marek samochodów (malejąco)",
col.names = c("Marka","liczba aut","% "))| Marka | liczba aut | % | |
|---|---|---|---|
| 3 | BMW | 108 | 8.65 |
| 2 | Audi | 96 | 7.69 |
| 43 | Volvo | 85 | 6.81 |
| 42 | Volkswagen | 81 | 6.49 |
| 30 | Opel | 80 | 6.41 |
| 33 | Renault | 80 | 6.41 |
| 13 | Ford | 77 | 6.17 |
| 41 | Toyota | 64 | 5.13 |
| 26 | Mercedes-Benz | 55 | 4.41 |
| 7 | Citroën | 49 | 3.93 |
| 15 | Hyundai | 48 | 3.85 |
| 31 | Peugeot | 43 | 3.45 |
| 19 | Kia | 39 | 3.12 |
| 14 | Honda | 33 | 2.64 |
| 29 | Nissan | 32 | 2.56 |
| 36 | Skoda | 32 | 2.56 |
| 12 | Fiat | 27 | 2.16 |
| 35 | Seat | 27 | 2.16 |
| 40 | Suzuki | 26 | 2.08 |
| 25 | Mazda | 23 | 1.84 |
| 28 | Mitsubishi | 18 | 1.44 |
| 6 | Chrysler | 16 | 1.28 |
| 8 | Dacia | 14 | 1.12 |
| 27 | MINI | 14 | 1.12 |
| 1 | Alfa Romeo | 12 | 0.96 |
| 5 | Chevrolet | 12 | 0.96 |
| 18 | Jeep | 11 | 0.88 |
| 39 | Subaru | 10 | 0.80 |
| 32 | Porsche | 6 | 0.48 |
| 34 | Saab | 5 | 0.40 |
| 10 | Dodge | 3 | 0.24 |
| 16 | Infiniti | 3 | 0.24 |
| 23 | Land Rover | 3 | 0.24 |
| 24 | Lexus | 3 | 0.24 |
| 4 | Cadillac | 2 | 0.16 |
| 9 | Daihatsu | 2 | 0.16 |
| 17 | Jaguar | 2 | 0.16 |
| 22 | Lancia | 2 | 0.16 |
| 11 | Ferrari | 1 | 0.08 |
| 20 | Lada | 1 | 0.08 |
| 21 | Lamborghini | 1 | 0.08 |
| 37 | Smart | 1 | 0.08 |
| 38 | SsangYong | 1 | 0.08 |
library(plotly)##
## Dołączanie pakietu: 'plotly'## Następujący obiekt został zakryty z 'package:ggplot2':
##
## last_plot## Następujący obiekt został zakryty z 'package:rio':
##
## export## Następujący obiekt został zakryty z 'package:stats':
##
## filter## Następujący obiekt został zakryty z 'package:graphics':
##
## layoutobiekt <- ggplot(autka_Marki, aes(x=Liczba_aut,y=Marka_pojazdu, fill=Marka_pojazdu)) +
geom_bar(stat="identity")
ggplotly(obiekt)Najpopularniejszymi markami samochodów wystawianymi na sprzedarz w okolicach Lublina to:
BMW 8.65%
Audi 7.69%
Volvo 6.81
Model pojazdu
Analizować będziemy tylko 10 najpopularniejszych marek ( z powyższego zestawienia ). Będą to:
BMW
top10 <- head(autka_Marki_sort[,1],n=10)
autka_Model <- autka_dt[autka_dt$Marka_pojazdu==top10[1],]|>
count(Model_pojazdu) |>
data.frame()
colnames(autka_Model)[2] <- "Liczba_aut"
autka_Model_sort <- autka_Model[order(autka_Model$Liczba_aut, decreasing=T), ]
autka_Model_sort_procent <- round((autka_Model_sort$Liczba_aut*100)/sum(autka_Model$Liczba_aut),2)
cbind(autka_Model_sort,autka_Model_sort_procent)|>
kable(caption = paste("Liczba aut poszczególnych modeli marki",top10[1],"(malejąco)",sep=" "),
col.names = c("Model","liczba aut","% ")) | Model | liczba aut | % | |
|---|---|---|---|
| 4 | Seria 3 | 29 | 26.85 |
| 6 | Seria 5 | 24 | 22.22 |
| 12 | X3 | 13 | 12.04 |
| 2 | Seria 1 | 7 | 6.48 |
| 14 | X5 | 7 | 6.48 |
| 8 | Seria 7 | 6 | 5.56 |
| 10 | X1 | 5 | 4.63 |
| 3 | Seria 2 | 3 | 2.78 |
| 13 | X4 | 3 | 2.78 |
| 5 | Seria 4 | 2 | 1.85 |
| 11 | X2 | 2 | 1.85 |
| 15 | X5 M | 2 | 1.85 |
| 16 | X6 | 2 | 1.85 |
| 1 | 3GT | 1 | 0.93 |
| 7 | Seria 6 | 1 | 0.93 |
| 9 | Seria 8 | 1 | 0.93 |
obiekt <- ggplot(autka_Model, aes(x =Liczba_aut,y=Model_pojazdu, fill=Model_pojazdu)) +
geom_bar(stat="identity")
ggplotly(obiekt)Audi
autka_Model2 <- autka_dt[autka_dt$Marka_pojazdu==top10[2],]|>
count(Model_pojazdu) |>
data.frame()
colnames(autka_Model2)[2] <- "Liczba_aut"
autka_Model_sort2 <- autka_Model2[order(autka_Model2$Liczba_aut, decreasing=T), ]
autka_Model_sort_procent2 <- round((autka_Model_sort2$Liczba_aut*100)/sum(autka_Model2$Liczba_aut),2)
cbind(autka_Model_sort2,autka_Model_sort_procent2)|>
kable(caption = paste("Liczba aut poszczególnych modeli marki",top10[2],"(malejąco)",sep=" "),
col.names = c("Model","liczba aut","% ")) | Model | liczba aut | % | |
|---|---|---|---|
| 4 | A4 | 30 | 31.25 |
| 6 | A6 | 16 | 16.67 |
| 3 | A3 | 15 | 15.62 |
| 11 | Q5 | 7 | 7.29 |
| 9 | A8 | 6 | 6.25 |
| 12 | Q7 | 6 | 6.25 |
| 5 | A5 | 4 | 4.17 |
| 10 | Q3 | 3 | 3.12 |
| 2 | A1 | 2 | 2.08 |
| 8 | A7 | 2 | 2.08 |
| 1 | 80 | 1 | 1.04 |
| 7 | A6 Allroad | 1 | 1.04 |
| 13 | Q8 | 1 | 1.04 |
| 14 | S3 | 1 | 1.04 |
| 15 | TT | 1 | 1.04 |
obiekt <- ggplot(autka_Model2, aes(x =Liczba_aut,y=Model_pojazdu, fill=Model_pojazdu)) +
geom_bar(stat="identity")
ggplotly(obiekt)Volvo
autka_Model3 <- autka_dt[autka_dt$Marka_pojazdu==top10[3],]|>
count(Model_pojazdu) |>
data.frame()
colnames(autka_Model3)[2] <- "Liczba_aut"
autka_Model_sort <- autka_Model3[order(autka_Model3$Liczba_aut, decreasing=T), ]
autka_Model_sort_procent <- round((autka_Model_sort$Liczba_aut*100)/sum(autka_Model3$Liczba_aut),2)
cbind(autka_Model_sort,autka_Model_sort_procent)|>
kable(caption = paste("Liczba aut poszczególnych modeli marki",top10[3],"(malejąco)",sep=" "),
col.names = c("Model","liczba aut","% ")) | Model | liczba aut | % | |
|---|---|---|---|
| 12 | XC 60 | 18 | 21.18 |
| 7 | V50 | 13 | 15.29 |
| 6 | V40 | 11 | 12.94 |
| 3 | S60 | 9 | 10.59 |
| 8 | V60 | 7 | 8.24 |
| 14 | XC 90 | 7 | 8.24 |
| 9 | V70 | 5 | 5.88 |
| 4 | S80 | 4 | 4.71 |
| 11 | XC 40 | 4 | 4.71 |
| 1 | C30 | 2 | 2.35 |
| 2 | S40 | 2 | 2.35 |
| 5 | Seria 900 | 1 | 1.18 |
| 10 | V90 Cross Country | 1 | 1.18 |
| 13 | XC 70 | 1 | 1.18 |
obiekt <- ggplot(autka_Model3, aes(x =Liczba_aut,y=Model_pojazdu, fill=Model_pojazdu)) +
geom_bar(stat="identity")
obiekt|>ggplotly()Volkswagen
autka_Model4 <- autka_dt[autka_dt$Marka_pojazdu==top10[4],]|>
count(Model_pojazdu) |>
data.frame()
colnames(autka_Model4)[2] <- "Liczba_aut"
autka_Model_sort <- autka_Model4[order(autka_Model4$Liczba_aut, decreasing=T), ]
autka_Model_sort_procent <- round((autka_Model_sort$Liczba_aut*100)/sum(autka_Model4$Liczba_aut),2)
cbind(autka_Model_sort,autka_Model_sort_procent)|>
kable(caption = paste("Liczba aut poszczególnych modeli marki",top10[4],"(malejąco)",sep=" "),
col.names = c("Model","liczba aut","% ")) | Model | liczba aut | % | |
|---|---|---|---|
| 9 | Passat | 22 | 27.16 |
| 4 | Golf | 19 | 23.46 |
| 14 | Tiguan | 11 | 13.58 |
| 10 | Polo | 8 | 9.88 |
| 5 | Golf Plus | 3 | 3.70 |
| 16 | Touran | 3 | 3.70 |
| 7 | Jetta | 2 | 2.47 |
| 8 | New Beetle | 2 | 2.47 |
| 12 | Sharan | 2 | 2.47 |
| 13 | T-Roc | 2 | 2.47 |
| 1 | Arteon | 1 | 1.23 |
| 2 | Crafter | 1 | 1.23 |
| 3 | Garbus | 1 | 1.23 |
| 6 | Golf Sportsvan | 1 | 1.23 |
| 11 | Scirocco | 1 | 1.23 |
| 15 | Touareg | 1 | 1.23 |
| 17 | Transporter | 1 | 1.23 |
obiekt <- ggplot(autka_Model4, aes(x =Liczba_aut,y=Model_pojazdu, fill=Model_pojazdu)) +
geom_bar(stat="identity")
obiekt|>ggplotly()Opel
autka_Model5 <- autka_dt[autka_dt$Marka_pojazdu==top10[5],]|>
count(Model_pojazdu) |>
data.frame()
colnames(autka_Model5)[2] <- "Liczba_aut"
autka_Model_sort <- autka_Model5[order(autka_Model5$Liczba_aut, decreasing=T), ]
autka_Model_sort_procent <- round((autka_Model_sort$Liczba_aut*100)/sum(autka_Model5$Liczba_aut),2)
cbind(autka_Model_sort,autka_Model_sort_procent)|>
kable(caption = paste("Liczba aut poszczególnych modeli marki",top10[5],"(malejąco)",sep=" "),
col.names = c("Model","liczba aut","% ")) | Model | liczba aut | % | |
|---|---|---|---|
| 3 | Astra | 19 | 23.75 |
| 9 | Insignia | 15 | 18.75 |
| 5 | Corsa | 12 | 15.00 |
| 10 | Meriva | 7 | 8.75 |
| 14 | Vectra | 6 | 7.50 |
| 16 | Zafira | 6 | 7.50 |
| 11 | Mokka | 5 | 6.25 |
| 6 | Crossland X | 2 | 2.50 |
| 1 | Adam | 1 | 1.25 |
| 2 | Antara | 1 | 1.25 |
| 4 | Combo | 1 | 1.25 |
| 7 | Grandland | 1 | 1.25 |
| 8 | Grandland X | 1 | 1.25 |
| 12 | Signum | 1 | 1.25 |
| 13 | Tigra | 1 | 1.25 |
| 15 | Vivaro | 1 | 1.25 |
obiekt <- ggplot(autka_Model5, aes(x =Liczba_aut,y=Model_pojazdu, fill=Model_pojazdu)) +
geom_bar(stat="identity")
obiekt|>ggplotly()Typ nadwozia
autka_nadwozie <- autka|>
count(Typ_nadwozia) |>
data.frame()
colnames(autka_nadwozie)[2] <- "Liczba_aut"
autka_nadwozie_sort <- autka_nadwozie[order(autka_nadwozie$Liczba_aut, decreasing=T), ]
autka_nadwozie_sort_procent <- round((autka_nadwozie_sort$Liczba_aut*100)/sum(autka_nadwozie$Liczba_aut),2)
cbind(autka_nadwozie_sort,autka_nadwozie_sort_procent)|>
kable(caption = paste("Typ nadwozia","(malejąco)",sep=" "),
col.names = c("Typ nadwozia","liczba aut","% "))| Typ nadwozia | liczba aut | % | |
|---|---|---|---|
| 8 | SUV | 312 | 25.00 |
| 5 | Kombi | 243 | 19.47 |
| 9 | Sedan | 200 | 16.03 |
| 6 | Kompakt | 193 | 15.46 |
| 7 | Minivan | 125 | 10.02 |
| 2 | Auta miejskie | 110 | 8.81 |
| 3 | Coupe | 35 | 2.80 |
| 1 | Auta małe | 20 | 1.60 |
| 4 | Kabriolet | 10 | 0.80 |
obiekt <-ggplot(autka_nadwozie,aes(x=Liczba_aut,y=Typ_nadwozia,fill=Typ_nadwozia)) +
geom_bar(stat="identity")
obiekt|>ggplotly()Najpopularniejszym typem nadwozia okazał się SUV.
Ludzie jeżdżą samochodami typu SUV, ponieważ są one bardziej wytrzymałe i mają lepszą przyczepność w trudnych warunkach. SUV-y mają również większą przestrzeń wewnątrz, co oznacza, że są one bardziej funkcjonalne dla rodzin i osób, które potrzebują dużej ilości miejsca do przewożenia przedmiotów. Są również bardziej wygodne i bezpieczne, ponieważ mają wyższe siedzenia i wyższy próg, co zapewnia lepszą widoczność i stabilność.
Rodzaj Paliwa
autka_paliwo <- autka|>
count(Rodzaj_paliwa) |>
data.frame()
colnames(autka_paliwo)[2] <- "Liczba_aut"
autka_paliwo_sort <- autka_paliwo[order(autka_paliwo$Liczba_aut, decreasing=T), ]
autka_paliwo_sort_procent <- round((autka_paliwo_sort$Liczba_aut*100)/sum(autka_paliwo$Liczba_aut),2)
cbind(autka_paliwo_sort,autka_paliwo_sort_procent)|>
kable(caption = paste("Rodzaj paliwa","(malejąco)",sep=" "),
col.names = c("Rodzaj paliwa","liczba aut","% "))| Rodzaj paliwa | liczba aut | % | |
|---|---|---|---|
| 1 | Benzyna | 572 | 45.83 |
| 3 | Diesel | 544 | 43.59 |
| 2 | Benzyna+LPG | 97 | 7.77 |
| 5 | Hybryda | 32 | 2.56 |
| 4 | Elektryczny | 3 | 0.24 |
obiekt <-ggplot(autka_paliwo,aes(y=Liczba_aut,x=Rodzaj_paliwa,fill=Rodzaj_paliwa)) +
geom_bar(stat="identity")
obiekt|>ggplotly()Widzimy, że zdecydowaną większością są samochody napędzane benzyną. Ma ona wiele zalet, w tym wyższą sprawność niż inne paliwa, a także łatwość użytkowania. Jest także bardziej przystępna cenowo niż inne paliwa, co czyni ją bardziej opłacalnym dla wielu kierowców. Niestety, benzyna ma też swoje wady, w tym wyższe emisje spalin i krótszy czas pracy niż inne paliwa. Związane jest to ze spalaniem benzyny, która jest bardziej skomplikowana i wymaga większej ilości energii, aby została spalona w porównaniu do innych.
Silniki napędzane ropą mają zazwyczaj większą moc niż te napędzane innymi paliwami. Oznacza to, że samochody napędzane ropą są lepsze do jazdy po autostradzie i pozwalają uzyskać wyższą prędkość maksymalną. Silniki napędzane ropą mają zazwyczaj wyższą wydajność niż silniki napędzane innymi paliwami. Oznacza to, że samochody napędzane ropą są bardziej ekonomiczne w zużyciu paliwa, dzięki czemu użytkownicy mogą zaoszczędzić na kosztach paliwa. Ogromną zaletą jest również długa żywotność silnika, dlatego właśnie są one tak popularne.
Jest on znacznie bardziej ekologiczny niż benzyna lub olej napędowy, ale jest również trudniejszy w instalacji. Wymaga on budowy stacji do tankowania gazu, w których należy również przechowywać go w bezpieczny sposób. Ponadto samochody napędzane gazem ziemnym są droższe w produkcji niż samochody napędzane tradycyjnymi paliwami, co sprawia, że producenci samochodów niechętnie sięgają po te technologie.
Hybrydy są relatywnie nowymi pojazdami na drogach, a ich popularność stale rośnie. Jednak jest to nadal rynek raczkujący, a producenci nadal udoskonalają technologię, aby zapewnić kierowcom najlepsze wrażenia z jazdy. Hybrydy są wyraźnie droższe niż tradycyjne samochody spalinowe, dlatego wielu kierowców może się wahać przy ich zakupie. Wraz z pojawieniem się nowszych technologii hybrydowe, które są coraz bardziej wydajne i wygodne, oczekuje się, że ich popularność będzie dalej rosnąć. Dlaczego jest ich tak mało? - Wysoka cena. Hybrydy są zazwyczaj droższe od tradycyjnych samochodów spalinowych. - Trudność w utrzymaniu. Hybrydy są zazwyczaj trudniejsze w utrzymaniu niż tradycyjne samochody spalinowe, ponieważ wymagają specjalnych części i narzędzi do serwisowania. Ograniczony zasięg. Hybrydy zazwyczaj mają ograniczony zasięg, ponieważ akumulatory szybko się wyczerpują.
Najmniej jest samochodów elektrycznych (3 modele). Samochody elektryczne są wciąż na wczesnym etapie rozwoju i ich popularność jest ograniczona. Głównymi przyczynamisą niska dostępność, wysokie koszty zakupu, ograniczone możliwości ładowania, krótki zasięg i brak infrastruktury ładowania. W wielu krajach nie ma dostatecznego poziomu inwestycji w infrastrukturę do ładowania, a samochody elektryczne są obciążone wysokimi podatkami. W wielu krajach wciąż brakuje odpowiednich środków pomocy publicznej na zakup samochodów elektrycznych, co ogranicza dostępność tych pojazdów. Dodatkową przyczyną małej ilości samochodów, jest to, że nasza baza składa się w większości z samochodów będących eksploatowacnych przez przynajmniej jednego właściciela.
Kolor
autka_kolor <- autka|>
count(Kolor) |>
data.frame()
colnames(autka_kolor)[2] <- "Liczba_aut"
autka_kolor_sort <- autka_kolor[order(autka_kolor$Liczba_aut, decreasing=T), ]
autka_kolor_sort_procent <- round((autka_kolor_sort$Liczba_aut*100)/sum(autka_kolor$Liczba_aut),2)
cbind(autka_kolor_sort,autka_kolor_sort_procent)|>
kable(caption = paste("Kolory samochodów","(malejąco)",sep=" "),
col.names = c("Kolor","liczba aut","% "))| Kolor | liczba aut | % | |
|---|---|---|---|
| 6 | Czarny | 301 | 24.12 |
| 14 | Szary | 203 | 16.27 |
| 13 | Srebrny | 181 | 14.50 |
| 2 | Biały | 169 | 13.54 |
| 11 | Niebieski | 86 | 6.89 |
| 9 | Granatowy | 54 | 4.33 |
| 10 | Inny kolor | 52 | 4.17 |
| 7 | Czerwony | 51 | 4.09 |
| 4 | Brązowy | 38 | 3.04 |
| 15 | Zielony | 25 | 2.00 |
| 1 | Beżowy | 22 | 1.76 |
| 16 | Złoty | 22 | 1.76 |
| 3 | Bordowy | 19 | 1.52 |
| 5 | Błękitny | 8 | 0.64 |
| 12 | Pomarańczowy | 7 | 0.56 |
| 17 | Żółty | 7 | 0.56 |
| 8 | Fioletowy | 3 | 0.24 |
obiekt <-ggplot(autka_kolor,aes(x=Liczba_aut,y=Kolor,fill=Kolor)) +
geom_bar(stat="identity", fill=c("beige", "white","darkred",
"brown","lightblue","black",
"red","purple","darkblue",
"#00A693","blue", "orange",
"aliceblue","grey","green",
"gold","yellow"),
colour="black")
obiekt|>ggplotly()To, jaki kolor samochodu wybiera kupujący jest wyborem subiektywnym, są jednak pewne przesłanki, dlaczego ludzie pewne kolory wybierają cześciej od pozostałych:
Czarny kolor samochodu osobowego jest zazwyczaj wybierany ze względu na jego wszechstronność i uniwersalność. Jest on jednym z najbardziej popularnych kolorów samochodów osobowych, ponieważ łatwo dopasowuje się do innych kolorów, wygląda elegancko i jest łatwy w czyszczeniu. Ponadto uważany jest za symbol luksusu i statusu.
Szary kolor samochodu osobowego jest popularny, ponieważ jest uniwersalny. Nie jest ani zbyt jasny ani zbyt ciemny, dzięki czemu pasuje do wielu stylów wystroju wnętrz. Jest również znacznie łatwiejszy do utrzymania w czystości niż inne jasne kolory. Dodatkowo, może on pomóc w zmniejszeniu temperatury wewnątrz kabiny, dzięki czemu pojazd jest bardziej komfortowy.
Srebrny podobnie jak szary kolor jest popularny, ponieważ jest bardzo uniwersalny i pasuje do wielu różnych typów samochodów. Jest również łatwy w utrzymaniu, ponieważ nie ma potrzeby częstego polerowania i zabezpieczania.
Białe samochody są popularne, ponieważ są zazwyczaj bardziej dostępne w niższych cenach (sprawdzimy czy to prawda) i są łatwiejsze w utrzymaniu w czystości. Ponadto, biały kolor samochodu może pomóc w nadawaniu mu nowoczesnego wyglądu, który może zwiększyć jego wartość. Biały odcień jest również popularny ze względu na jego uniwersalny wygląd, który może pasować do każdego rodzaju otoczenia.
Zastanówmy się jeszcze, dlaczego kolory takie jak pomarańczowy, żółty i filetowy są tak mało popularne? Oto kilka sugestii.
Fioletowe samochody są zazwyczaj wytwarzane z tańszych materiałów, co prowadzi do niższej jakości. Są również bardziej podatne na awarie i korozję, co oznacza większe koszty utrzymania. Ponadto, w porównaniu do nowych samochodów, mają mniej opcji i technologii, które są dostępne w nowych samochodach, przez co są mało atrakcyjne dla kupujących.
Pomarańczowy kolor samochodu jest zwykle uważany za zbyt odważny, zazwyczaj ludzie wolą kupować bardziej tradycyjne kolory, takie jak srebrny, czarny lub biały. Ponadto, kolor pomarańczowy jest trudniejszy do utrzymania w czystości, ponieważ łatwo się brudzi i trudniej go wyczyścić.
Ludzie nie kupują żółtych samochodów, ponieważ uważają, że jest to mało popularny kolor. Żółty jest postrzegany jako niepraktyczny i nieatrakcyjny wybór, ponieważ jest trudny do dopasowania do innych kolorów lub wzorów i może być uważany za zbyt „śmiały” lub „niesmaczny”. Dlatego jest ich tak mało.
- Zmienne ilościowe
autka_liczbowe <- autka_dt[,c(4,5,6,8,11)]
liczbowe <- apply(autka_liczbowe,2,summary)
liczbowe <- rbind(liczbowe,St.dev=apply(autka_liczbowe,2,sd))
rownames(liczbowe) <- c('minimum','kwantyl dolny','mediana','średnia','kwantyl górny','maksimum','odchylenie standardowe')
as.data.frame(liczbowe) |>
kable(caption="Statystyki parametrów typu liczbowego",
digits = 0)| Rok_produkcji | Przebieg | Pojemność_skokowa | Moc | Cena | |
|---|---|---|---|---|---|
| minimum | 1961 | 0 | 0 | 30 | 1999 |
| kwantyl dolny | 2007 | 102020 | 1560 | 110 | 19700 |
| mediana | 2012 | 176000 | 1896 | 136 | 36900 |
| średnia | 2011 | 172701 | 1933 | 154 | 59409 |
| kwantyl górny | 2016 | 237350 | 1997 | 174 | 63346 |
| maksimum | 2023 | 2230000 | 6208 | 612 | 1626016 |
| odchylenie standardowe | 6 | 114166 | 728 | 74 | 90907 |
Zmienne typu liczbowego
Rok produkcji
as.data.frame(liczbowe[,1]) |>
kable(caption="Rok produkcji",
digits = 0,
col.names = "Rok produkcji")| Rok produkcji | |
|---|---|
| minimum | 1961 |
| kwantyl dolny | 2007 |
| mediana | 2012 |
| średnia | 2011 |
| kwantyl górny | 2016 |
| maksimum | 2023 |
| odchylenie standardowe | 6 |
hist (autka_liczbowe[,1],
col=c("red", "green"),
xlab="Rok produkcji",
ylab="Częstotliwość",
las =2,
main="Histogram Roku Produkcji aut")
boxplot(autka_dt$Rok_produkcji)
Widzimy, że 50% samochodów zostało wyprodukowanych w latach 2007-2016. Warością środkową są samochody z 2012 roku.
autka[autka$Rok_produkcji==1961,] |>
kable()| id | Marka_pojazdu | Model_pojazdu | Rok_produkcji | Przebieg | Pojemność_skokowa | Rodzaj_paliwa | Moc | Typ_nadwozia | Kolor | Cena | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1171 | 1171 | Fiat | Inny | 1961 | 79 600 km | 1 295 cm3 | Benzyna | 65 KM | Sedan | Niebieski | 25900 |
Najstarszym samochodem okazał się nr. 1171. Samochód został wyprodukowany 61 lat temu! Na podstawie histogramu wiemy, że naprawdę jest to rzadkie zjawisko. Wiemy, że samochod jest marki “FIAT”, niestety sprzedający nie podał konkretnego modelu auta. Wiemy jedynie, że jest marki FIAT. Po przeszukaniu informacji w internecie (link), udało się znaleźć informacje, że firma Fiat w 1961 roku produkowała tylko i wyłącznie poniższe samochody: [zdjecie1] Dodatkowo wiemy, że typ nadwozia to sedan, a więc zwężamy zbiór możliwości do: [zdjecie2] Analizując poszczególne modele, okazuje się że moc silnika naszego auta w porównaniu ze specyfikacjami modeli 1100 oraz 1300 jest za duża, natomiast model 1800/2100 według danych ze strony powinien mieć większą pojemność skokową silnika. Tak więc prawdopodobnie samochód z naszej bazy to właśnie Fiat 1500. Dane z bazy pokrywają się z podaną specyfikacją pojazdu. [zdjecie3]
(ciekawostka - samochody zabytkowe)
Aby auto zostało uznane za zabytek, musi spełnić kilka wytycznych, tj.:
ma przynajmniej 30 lat,
jest modelem wycofanym z produkcji przynajmniej 15 lat temu,
jest zbudowane przynajmniej w 75 proc. z oryginalnych części (jeżeli nie spełnia tego warunku, zostanie uznany za replikę oryginalnego modelu).
Wprawdzie zdarza się, że konserwator może wpisać do rejestru samochód, który nie ma 30 lat, ale tylko pod pewnymi warunkami. Jest to możliwe, jeżeli jest to nietypowa konstrukcja. To samo dotyczy szczególnego pochodzenia zastosowanych technologii i rozwiązań. Dlatego, jeżeli masz wyjątkowy pojazd, zwłaszcza należący w przeszłości do ważnej osobowości lub kogoś, kto osiągnął sukces sportowy itp., również możesz zarejestrować go jako samochód zabytkowy.
Sprawdźmy więc, ile samochodów z bazy spełnia pierwszy warunek (produkcja przed 1992 rokiem):
dim(autka[autka$Rok_produkcji[]<=1992,])## [1] 10 11autka[autka$Rok_produkcji[]<=1992,] |>
kable( row.names = NA)| id | Marka_pojazdu | Model_pojazdu | Rok_produkcji | Przebieg | Pojemność_skokowa | Rodzaj_paliwa | Moc | Typ_nadwozia | Kolor | Cena | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 377 | 377 | Mercedes-Benz | SL | 1986 | 145 000 km | 4 973 cm3 | Benzyna | 326 KM | Kabriolet | Beżowy | 86000 |
| 390 | 390 | Citroën | CX | 1981 | 1 230 000 km | 2 499 cm3 | Benzyna | 137 KM | Sedan | Szary | 9999 |
| 899 | 899 | Ferrari | Testarossa | 1992 | 23 374 km | 4 943 cm3 | Benzyna | 428 KM | Coupe | Czerwony | 1050000 |
| 905 | 905 | Ford | Sierra | 1991 | 110 919 km | 1 998 cm3 | Benzyna | 120 KM | Sedan | Czerwony | 16900 |
| 913 | 913 | BMW | Seria 7 | 1985 | 211 000 km | 2 788 cm3 | Benzyna | 183 KM | Sedan | Zielony | 35000 |
| 938 | 938 | Volvo | Seria 900 | 1992 | 149 500 km | 1 986 cm3 | Benzyna | 190 KM | Sedan | Czarny | 36900 |
| 992 | 992 | Chevrolet | Impala | 1974 | 60 000 km | 5 736 cm3 | Benzyna | 167 KM | Sedan | Niebieski | 64000 |
| 1165 | 1165 | Volkswagen | Garbus | 1985 | 10 000 km | 1 300 cm3 | Benzyna | 30 KM | Kabriolet | Inny kolor | 5500 |
| 1170 | 1170 | Toyota | Tercel | 1979 | 81 804 km | 1 279 cm3 | Benzyna | 65 KM | Auta miejskie | Inny kolor | 15900 |
| 1171 | 1171 | Fiat | Inny | 1961 | 79 600 km | 1 295 cm3 | Benzyna | 65 KM | Sedan | Niebieski | 25900 |
10 samochodów z bazy spełnia pierwszy warunek, aby mogły być uznane za auta zabytkowe.
Średni rok produkcji oraz mediana mają podobne wartości (2011/2012), oznacza to, że średnio oraz najcześciej sprzedawane są 10 letnie samochody.
Kwantyl dolny (2007) oraz górny (2016) infurmują, że 25% wszystkich samochodów została wyprodukowana przed 2007 rokiem, oraz 25% zostało wyprodokowanych po 2016 roku.
Dziwić może maksimum badanego parametru wynoszące 2023. (Dane pochodzą z października 2022)
autka[autka$Rok_produkcji[]==2023,] |>
kable()| id | Marka_pojazdu | Model_pojazdu | Rok_produkcji | Przebieg | Pojemność_skokowa | Rodzaj_paliwa | Moc | Typ_nadwozia | Kolor | Cena | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 613 | 613 | Dacia | Duster | 2023 | 9 km | 999 cm3 | Benzyna+LPG | 100 KM | SUV | Zielony | 86550 |
Dostrzegamy również, że samochód ma już przejechane 9 kilometrów. (czyżby był z przyszłości !? ). Całe to zamieszanie jest spowodowane tym, że salony samochodowe nie przypisują samochodom im dokładnej daty produkcji, a tak naprawdę datę modelową, stąd na koniec każdego roku w salonach dostępna jest opcja kupna samochodu, którego rocznik jest z roku następnego. Jeżeli kupującego interesuje od kiedy samochód jest w użytku, najdokłądniejszą informacje uzyska dzięki wyczytaniu z dowodu rejestracyjnego datę pierwszej rejestracji.
Porównanie roku produkcji poszczególnych marek
autka_dt|>
group_by(Marka_pojazdu) |>
summarise(minimim=min(Rok_produkcji),
średnia=mean(Rok_produkcji),
mediana=median(Rok_produkcji),
maksimum=max(Rok_produkcji)) |>
kable(caption = "Statystyki roku produkcji dla poszczególnych marek ")| Marka_pojazdu | minimim | średnia | mediana | maksimum |
|---|---|---|---|---|
| Alfa Romeo | 2008 | 2017.333 | 2019.0 | 2022 |
| Audi | 1993 | 2009.781 | 2009.5 | 2022 |
| BMW | 1985 | 2011.398 | 2012.0 | 2022 |
| Cadillac | 2004 | 2006.000 | 2006.0 | 2008 |
| Chevrolet | 1974 | 2006.250 | 2009.0 | 2013 |
| Chrysler | 2004 | 2013.188 | 2014.5 | 2019 |
| Citroën | 1981 | 2011.245 | 2011.0 | 2022 |
| Dacia | 2006 | 2017.500 | 2018.0 | 2023 |
| Daihatsu | 2005 | 2007.500 | 2007.5 | 2010 |
| Dodge | 2013 | 2015.000 | 2015.0 | 2017 |
| Ferrari | 1992 | 1992.000 | 1992.0 | 1992 |
| Fiat | 1961 | 2008.037 | 2008.0 | 2020 |
| Ford | 1991 | 2012.364 | 2012.0 | 2021 |
| Honda | 1998 | 2008.758 | 2008.0 | 2020 |
| Hyundai | 2007 | 2014.917 | 2014.5 | 2022 |
| Infiniti | 2011 | 2014.000 | 2015.0 | 2016 |
| Jaguar | 2014 | 2016.000 | 2016.0 | 2018 |
| Jeep | 1993 | 2007.455 | 2008.0 | 2018 |
| Kia | 2006 | 2015.308 | 2016.0 | 2022 |
| Lada | 1997 | 1997.000 | 1997.0 | 1997 |
| Lamborghini | 2005 | 2005.000 | 2005.0 | 2005 |
| Lancia | 2008 | 2011.000 | 2011.0 | 2014 |
| Land Rover | 1997 | 2001.000 | 1999.0 | 2007 |
| Lexus | 2000 | 2008.333 | 2008.0 | 2017 |
| Mazda | 2001 | 2011.565 | 2012.0 | 2020 |
| Mercedes-Benz | 1986 | 2009.164 | 2009.0 | 2022 |
| MINI | 2002 | 2011.929 | 2011.5 | 2022 |
| Mitsubishi | 2005 | 2010.889 | 2011.5 | 2017 |
| Nissan | 2000 | 2011.000 | 2011.0 | 2018 |
| Opel | 1998 | 2010.987 | 2011.0 | 2022 |
| Peugeot | 2004 | 2011.953 | 2011.0 | 2022 |
| Porsche | 2014 | 2017.833 | 2017.0 | 2022 |
| Renault | 1998 | 2011.925 | 2013.0 | 2022 |
| Saab | 1999 | 2004.800 | 2006.0 | 2008 |
| Seat | 2000 | 2010.185 | 2009.0 | 2020 |
| Skoda | 2001 | 2014.688 | 2016.0 | 2022 |
| Smart | 2016 | 2016.000 | 2016.0 | 2016 |
| SsangYong | 2017 | 2017.000 | 2017.0 | 2017 |
| Subaru | 2003 | 2010.200 | 2008.5 | 2019 |
| Suzuki | 1999 | 2010.269 | 2007.5 | 2022 |
| Toyota | 1979 | 2011.844 | 2012.5 | 2022 |
| Volkswagen | 1985 | 2010.469 | 2011.0 | 2022 |
| Volvo | 1992 | 2011.800 | 2012.0 | 2022 |
Przebieg
as.data.frame(liczbowe[,2]) |>
kable(caption="Przebieg",
col.names = "Przebieg")| Przebieg | |
|---|---|
| minimum | 0.0 |
| kwantyl dolny | 102019.5 |
| mediana | 176000.0 |
| średnia | 172701.2 |
| kwantyl górny | 237350.0 |
| maksimum | 2230000.0 |
| odchylenie standardowe | 114165.8 |
hist (autka_liczbowe[,2],
col=c("red", "green"),
xlab="Przebieg",
ylab="Częstotliwość",
las =2,
main="Histogram Przebiegu",
breaks = 20)
boxplot(autka_liczbowe[,2])
Na samym początku uwagę przykuwa znacząco odbiegające od wszystkich obserwacji maksimum
autka[autka$Przebieg=="2 230 000 km",] |>
kable()| id | Marka_pojazdu | Model_pojazdu | Rok_produkcji | Przebieg | Pojemność_skokowa | Rodzaj_paliwa | Moc | Typ_nadwozia | Kolor | Cena | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 430 | 430 | Hyundai | Tucson | 2008 | 2 230 000 km | 1 975 cm3 | Benzyna | 141 KM | SUV | Czarny | 18900 |
(2230000/14)/365## [1] 436.399214 letni samochód prejechał 2 230 000 km. Oznacza to, że średnio jeździł po 93 tys. km w ciągu roku oraz 436.3992172 km na dzień. Jako ciekawostka można dodać, że aby okrążyć ziemię wazdłuż równika, trzeba przejechać około 40 075 km. Nasz samochód mógłby okrążyć ziemię około 56 razy. Jako kolejna ciekawostka dla zwizualizowania jak wielka jest to odległość dodajmy, że odległość z Ziemi na Księżyc wynisi 384 400 km.
Wydaje się to dosyć nierealne i prawdopodobnie sprzedający omyłkowo wpisał o jedno “0” za dużo przy wystawianiu ogłoszenia.
Sprawdzmy srednia samochodów z 2008 roku bez naszej wartości odstającej:
przebieg <- autka_liczbowe[,2]
przebieg <- przebieg[-430]
przebieg[430]## [1] 320000median(przebieg)## [1] 176000mean(przebieg)## [1] 171051.4max(przebieg)## [1] 1230000Widzimy, że średnia oraz mediana wynoszą około 173 tys. Przyjmijmy więc opcję, że sprzedający się pomylił i podał dzeisięciokrotność przebiegu auta.
Na wykresie ramka wąsy widzimy jeszcze jedną obserwacje odstającą. Przyjżyjmy się jej dokładniej:
autka[autka$Przebieg=="1 230 000 km",] |>
kable()| id | Marka_pojazdu | Model_pojazdu | Rok_produkcji | Przebieg | Pojemność_skokowa | Rodzaj_paliwa | Moc | Typ_nadwozia | Kolor | Cena | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 390 | 390 | Citroën | CX | 1981 | 1 230 000 km | 2 499 cm3 | Benzyna | 137 KM | Sedan | Szary | 9999 |
W tym przypadku widzimy jednak, że jest to auto 41 letnie, więc średnio jeździło 30 tys km rocznie. Wydaje się to prawdopodobne, dlatego zostawiamy tak jak jest. Zmieniamy tylko i wyłącznie samochód o identyfikatorze 430.
#przebieg to kolumna 5
autka_dt[430,5] <- 223000
autka_liczbowe <- autka_dt[,c(4,5,6,8,11)]
liczbowe <- apply(autka_liczbowe,2,summary)
liczbowe <- rbind(liczbowe,St.dev=apply(autka_liczbowe,2,sd))
rownames(liczbowe) <- c('minimum','kwantyl dolny','mediana','średnia','kwantyl górny','maksimum','odchylenie standardowe')Wyświetlmy więc statystyki ponownie dla zmionionej obserwacji 430.
as.data.frame(liczbowe[,2]) |>
kable(caption="Przebieg",
col.names = "Przebieg")| Przebieg | |
|---|---|
| minimum | 0.0 |
| kwantyl dolny | 102019.5 |
| mediana | 176000.0 |
| średnia | 171093.0 |
| kwantyl górny | 237293.2 |
| maksimum | 1230000.0 |
| odchylenie standardowe | 98179.1 |
hist (autka_liczbowe[,2],
col=c("red", "green"),
xlab="Przebieg",
ylab="Częstotliwość",
las =2,
main="Histogram Przebiegu")
boxplot(autka_dt$Przebieg)
Wartośćią środkową jest 176 tys. przejechanych km. Średnio samochody wystawione na sprzedarz przejechały 171 tys kilometrów. 25% samochodów ma przebieg mniejszy niż 102 tys. kilometrów oraz 25% samochodów ma przebieg większy niż 237 tys. kilometrów.
Spróbujmy teraz oszacować, jaki przebieg powinien mieć samochód, zależnie od “wieku”.
autka_dt|>
group_by(Rok_produkcji) |>
summarise(minimim=min(Przebieg),
średnia=mean(Przebieg),
mediana=median(Przebieg),
maksimum=max(Przebieg)) |>
kable(caption = "Statystyki przebiegu zależne od roku produckji")| Rok_produkcji | minimim | średnia | mediana | maksimum |
|---|---|---|---|---|
| 1961 | 79600 | 79600.000 | 79600.0 | 79600 |
| 1974 | 60000 | 60000.000 | 60000.0 | 60000 |
| 1979 | 81804 | 81804.000 | 81804.0 | 81804 |
| 1981 | 1230000 | 1230000.000 | 1230000.0 | 1230000 |
| 1985 | 10000 | 110500.000 | 110500.0 | 211000 |
| 1986 | 145000 | 145000.000 | 145000.0 | 145000 |
| 1991 | 110919 | 110919.000 | 110919.0 | 110919 |
| 1992 | 23374 | 86437.000 | 86437.0 | 149500 |
| 1993 | 240000 | 390625.000 | 435000.0 | 452500 |
| 1994 | 158287 | 158287.000 | 158287.0 | 158287 |
| 1995 | 282000 | 282000.000 | 282000.0 | 282000 |
| 1997 | 190000 | 283433.429 | 285000.0 | 356100 |
| 1998 | 139000 | 235307.143 | 249250.0 | 346900 |
| 1999 | 131226 | 240360.091 | 240154.0 | 334000 |
| 2000 | 133000 | 314045.286 | 318509.0 | 456696 |
| 2001 | 111403 | 269955.417 | 257381.0 | 431000 |
| 2002 | 48745 | 238826.421 | 239985.0 | 424590 |
| 2003 | 77000 | 235796.800 | 234072.0 | 520000 |
| 2004 | 84481 | 237040.825 | 247550.0 | 369400 |
| 2005 | 46950 | 239112.667 | 241000.0 | 435000 |
| 2006 | 66423 | 219111.250 | 216816.0 | 400000 |
| 2007 | 98000 | 237766.260 | 226445.0 | 430000 |
| 2008 | 81000 | 229085.388 | 226000.0 | 379562 |
| 2009 | 95800 | 222805.662 | 224000.0 | 485000 |
| 2010 | 47166 | 210377.514 | 207750.0 | 470000 |
| 2011 | 84500 | 204057.301 | 209000.0 | 305000 |
| 2012 | 26326 | 186180.606 | 190000.0 | 371893 |
| 2013 | 25029 | 171900.600 | 177000.0 | 305000 |
| 2014 | 34745 | 170427.642 | 175000.0 | 330000 |
| 2015 | 33000 | 148359.035 | 142250.0 | 327000 |
| 2016 | 28800 | 118211.819 | 121968.5 | 195000 |
| 2017 | 14489 | 105866.171 | 96500.0 | 293000 |
| 2018 | 12962 | 86597.622 | 81808.0 | 184000 |
| 2019 | 11575 | 74254.833 | 55763.0 | 175000 |
| 2020 | 10600 | 42594.320 | 34000.0 | 220000 |
| 2021 | 924 | 16575.500 | 16750.0 | 41215 |
| 2022 | 0 | 2449.318 | 5.0 | 29000 |
| 2023 | 9 | 9.000 | 9.0 | 9 |
Aby było to praktyczne, dodajmy warunek, że szukamy aut do użytku, ale nienowego, czyli bierzmy pod uwagę auta z roczników 2002-2020 (młodsze niż 20 lat ale starsze niż dwuletnie).
#auta z roczników 2002-2020
autka_dt2 <- autka_dt[autka_dt$Rok_produkcji<=2020 &autka_dt$Rok_produkcji>=2002,]
autka_dt2[,"Rok_produkcji"] <- 2022-autka_dt2[,"Rok_produkcji"]
colnames(autka_dt2)[4] <- "Wiek"
autka_dt2|>
group_by(Wiek) |>
summarise(średnia=mean(Przebieg),
mediana=median(Przebieg),
odchylenie_standardowe=sd(Przebieg))|>
kable(caption = "Statystyki przebiegu zależne od wieku samochodu",
digits = 0,
align="c")| Wiek | średnia | mediana | odchylenie_standardowe |
|---|---|---|---|
| 2 | 42594 | 34000 | 42606 |
| 3 | 74255 | 55763 | 45284 |
| 4 | 86598 | 81808 | 44610 |
| 5 | 105866 | 96500 | 56227 |
| 6 | 118212 | 121968 | 43320 |
| 7 | 148359 | 142250 | 70773 |
| 8 | 170428 | 175000 | 60167 |
| 9 | 171901 | 177000 | 56217 |
| 10 | 186181 | 190000 | 65797 |
| 11 | 204057 | 209000 | 52528 |
| 12 | 210378 | 207750 | 66153 |
| 13 | 222806 | 224000 | 67686 |
| 14 | 229085 | 226000 | 58672 |
| 15 | 237766 | 226445 | 68998 |
| 16 | 219111 | 216816 | 67373 |
| 17 | 239113 | 241000 | 64953 |
| 18 | 237041 | 247550 | 65047 |
| 19 | 235797 | 234072 | 86916 |
| 20 | 238826 | 239985 | 79513 |
Przy pomocy tego oszacowania kupujący auto może zorientować się, jaki przebieg powienien mieć samochód w zależności od wieku. Zdarzają się niestety niegodziwcy, którzy “cofają” wskaźnik licznika aby sztucznie zwiększyć wartość auta. Dzięki powyższemu zestawieniu kupującemu łatwiej jest wykryć oszustwo.
Zbudujmy dodatkowo model liniowy zależności przebieku od wieku auta:
library(stats)
hist(autka_dt2$Przebieg)
md_przebieg <- lm(Przebieg~Wiek,autka_dt2)
summary(md_przebieg)##
## Call:
## lm(formula = Przebieg ~ Wiek, data = autka_dt2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -236967 -41700 -3018 37635 278635
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 59005.8 4614.9 12.79 <2e-16 ***
## Wiek 11335.3 399.2 28.39 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 62860 on 1103 degrees of freedom
## Multiple R-squared: 0.4223, Adjusted R-squared: 0.4217
## F-statistic: 806.2 on 1 and 1103 DF, p-value: < 2.2e-16library(lmtest)## Ładowanie wymaganego pakietu: zoo##
## Dołączanie pakietu: 'zoo'## Następujące obiekty zostały zakryte z 'package:base':
##
## as.Date, as.Date.numericplot(md_przebieg)
lmtest::raintest(md_przebieg)##
## Rainbow test
##
## data: md_przebieg
## Rain = 0.92444, df1 = 553, df2 = 550, p-value = 0.8218 cor.test(autka_dt2$Przebieg,autka_dt2$Wiek)##
## Pearson's product-moment correlation
##
## data: autka_dt2$Przebieg and autka_dt2$Wiek
## t = 28.394, df = 1103, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.6143971 0.6826379
## sample estimates:
## cor
## 0.6498251ggplot(aes(y=Przebieg,x=Wiek),data=autka_dt2)+
geom_point()+
geom_smooth(method = "lm")## `geom_smooth()` using formula = 'y ~ x'
Wniosek: Na podstawie modelu możęmy wnioskować, że w ciągu jednego roku samochód przejeżdża średnio 11 tys. km +/- 400km. Jednakże powyższe zestawienie oraz wykres dopasowania wskazują, że błąd jest zaniżony. Duża zmienność przebiegu uniemożliwia nam dokładniejsze oszacowania.
Dodatkowo patrząc na wartości odchyleń standardowych z tabeli dla poszczególnego wieku wyciągamy kolejne wnioski:
Odchylenie standardowe młodszych aut ( wiek:1-6 )waha się na poziomie 4000.
Ciekawa zależność pojawia się dla starszych samochodów. Występuje tu pewien “przeskok” i wartość oschylenia standardowego starszych roczników zaczyna oscylować wokół 6000.
Oczywiście jest to tylko wskazówka, a nie dowód. Na postawie analizy przebiegu kupujący ma przesłanki do tego, aby zapyać sprzedającego o szczegóły auta, które mogą wpływać na przebieg. np. cel użytku samochodu przez byłego właściciela.
Pojemność skokowa silnika
as.data.frame(liczbowe[,3]) |>
kable(caption="Pojemność skokowa",
digits = 0,
col.names = "Pojemność skokowa")| Pojemność skokowa | |
|---|---|
| minimum | 0 |
| kwantyl dolny | 1560 |
| mediana | 1896 |
| średnia | 1933 |
| kwantyl górny | 1997 |
| maksimum | 6208 |
| odchylenie standardowe | 728 |
hist (autka_liczbowe[,3],
col=c("red", "green"),
xlab="moc",
ylab="Częstotliwość",
las =2,
main="Histogram Pojemności skokowej aut")
boxplot(autka_dt$Pojemność_skokowa)
Statystyki mogą być nieco zaburzone ze względu na ustawione przez nas wartości zerowe w przypadku samochodów elektrycznych. Podczas analizowania pojemności skokowej, rozważmy więc wyłącznie samochody, które nimi nie są.
autka_dt3 <- autka_dt[-c(585,1000,1001),]
#liczbowe2 - bez elektrycznych
autka_liczbowe2 <- autka_dt3[,c(4,5,6,8,11)]
liczbowe2 <- apply(autka_liczbowe2,2,summary)
liczbowe2 <- rbind(liczbowe2,St.dev=apply(autka_liczbowe2,2,sd))
rownames(liczbowe2) <- c('minimum','kwantyl dolny','mediana','średnia','kwantyl górny','maksimum','odchylenie standardowe')
as.data.frame(liczbowe2[,3]) |>
kable(caption="Pojemność skokowa",
digits = 0,
col.names = "Pojemność skokowa")| Pojemność skokowa | |
|---|---|
| minimum | 875 |
| kwantyl dolny | 1560 |
| mediana | 1896 |
| średnia | 1938 |
| kwantyl górny | 1997 |
| maksimum | 6208 |
| odchylenie standardowe | 723 |
hist (autka_liczbowe2[,3],
col=c("red", "green"),
xlab="Pojemność skokowa",
ylab="Częstotliwość",
las =2,
main="Histogram Roku Produkcji aut")
boxplot(autka_dt3$Pojemność_skokowa)
Największa pojemność skokowa silnika wynosi: 6208. Owy silnik posiada poniższy samochód:
autka_dt[autka_dt$Pojemność_skokowa==max(autka_dt$Pojemność_skokowa),]## id Marka_pojazdu Model_pojazdu Rok_produkcji Przebieg Pojemność_skokowa
## 1201 1201 Mercedes-Benz Klasa C 2013 75543 6208
## Rodzaj_paliwa Moc Typ_nadwozia Kolor Cena
## 1201 Benzyna 507 Sedan Biały 365853Natomiast najmniejszy to: 875.
autka_dt3[autka_dt3$Pojemność_skokowa==min(autka_dt3$Pojemność_skokowa),]## id Marka_pojazdu Model_pojazdu Rok_produkcji Przebieg Pojemność_skokowa
## 790 790 Fiat 500 2015 33583 875
## Rodzaj_paliwa Moc Typ_nadwozia Kolor Cena
## 790 Benzyna 86 Kompakt Czarny 35000Średnio samochody mają pojemność 1.9.
Silnik 1,9 o pojemności (TDI) jest powszechnie uznawany za jeden z najlepszych, ponieważ jest wyjątkowo ekonomiczny, ma wysoką wydajność i moc, a jednocześnie cechuje go niski poziom emisji spalin. To wszystko sprawia, że jest bardzo popularny wśród kierowców, którzy korzystają z niego do samochodów osobowych i dostawczych. Dzięki swojej pojemności skokowej silnik 1,9 oferuje wystarczającą moc i wydajność w zakresie zużycia paliwa i emisji spalin. Dodatkowo jest on bardzo trwały i niezawodny, a jego konserwacja jest prosta i niedroga.
Moc silnika
Podobnie jak w poprzednim przypadku, samochody elektryczne nie będą brane pod uwagę w analizie.
as.data.frame(liczbowe2[,4]) |>
kable(caption="Moc silnika",
digits = 0,
col.names = "Moc silnika")| Moc silnika | |
|---|---|
| minimum | 30 |
| kwantyl dolny | 110 |
| mediana | 136 |
| średnia | 153 |
| kwantyl górny | 174 |
| maksimum | 612 |
| odchylenie standardowe | 73 |
hist (autka_liczbowe2[,4],
col=c("red", "green"),
xlab="Moc silnika",
ylab="Częstotliwość",
las =2,
main="Histogram Mocy silnika")
boxplot(autka_dt3$Moc)
Samochód z najwyższą mocą:
autka_dt3[autka_dt3$Moc==max(autka_dt3$Moc),]## id Marka_pojazdu Model_pojazdu Rok_produkcji Przebieg Pojemność_skokowa
## 900 900 Mercedes-Benz CL 2004 84481 5980
## Rodzaj_paliwa Moc Typ_nadwozia Kolor Cena
## 900 Benzyna 612 Coupe Czarny 2e+05Samochód z najniższą mocą:
autka_dt3[autka_dt3$Moc==min(autka_dt3$Moc),]## id Marka_pojazdu Model_pojazdu Rok_produkcji Przebieg Pojemność_skokowa
## 1165 1165 Volkswagen Garbus 1985 10000 1300
## Rodzaj_paliwa Moc Typ_nadwozia Kolor Cena
## 1165 Benzyna 30 Kabriolet Inny kolor 5500Średnia moc: 153KM.
Cena samochodu
as.data.frame(liczbowe[,5]) |>
kable(caption="Cena auts",
digits = 0,
col.names = "Cena auta")| Cena auta | |
|---|---|
| minimum | 1999 |
| kwantyl dolny | 19700 |
| mediana | 36900 |
| średnia | 59409 |
| kwantyl górny | 63346 |
| maksimum | 1626016 |
| odchylenie standardowe | 90907 |
hist (autka_liczbowe[,5],
col=c("red", "green"),
xlab="Cena",
ylab="Częstotliwość",
las =2,
main="Histogram Ceny aut",
breaks = 50)
boxplot(autka_dt$Cena)
Na podstawie histogramu widzimy , że rozkład Ceny ma rozkład o prawostronnej asymetrii. Większość wyników oscyluje w okolicach 37 tys. zł.
Zestawienie najdroższych samochodów:
#najwiekszy
autka_dt4 <-autka_dt[autka_dt$Cena>=500000,]
#sortowanie po cenie auta
autka_dt4 <- autka_dt4[ order(autka_dt4$Cena, decreasing=T), ]
autka_dt4 |>
kable()| id | Marka_pojazdu | Model_pojazdu | Rok_produkcji | Przebieg | Pojemność_skokowa | Rodzaj_paliwa | Moc | Typ_nadwozia | Kolor | Cena | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1024 | 1024 | Lamborghini | Murcielago | 2005 | 46950 | 6193 | Benzyna | 580 | Coupe | Biały | 1626016 |
| 851 | 851 | Porsche | 911 | 2022 | 405 | 3996 | Benzyna | 510 | Coupe | Czarny | 1097560 |
| 899 | 899 | Ferrari | Testarossa | 1992 | 23374 | 4943 | Benzyna | 428 | Coupe | Czerwony | 1050000 |
| 1001 | 1001 | Mercedes-Benz | EQS | 2022 | 0 | 0 | Elektryczny | 523 | Kompakt | Czarny | 783564 |
| 1224 | 1224 | BMW | Seria 8 | 2021 | 15000 | 4395 | Benzyna | 530 | Coupe | Szary | 579900 |
| 1004 | 1004 | Audi | A8 | 2022 | 5 | 2967 | Diesel | 286 | Sedan | Czarny | 533856 |
| 340 | 340 | Porsche | 718 Cayman | 2021 | 27700 | 3995 | Benzyna | 400 | Coupe | Żółty | 532000 |
Najtańszy samochód:
autka_dt[autka_dt$Cena==min(autka_dt$Cena),]## id Marka_pojazdu Model_pojazdu Rok_produkcji Przebieg Pojemność_skokowa
## 159 159 Honda Civic 1998 289000 1396
## Rodzaj_paliwa Moc Typ_nadwozia Kolor Cena
## 159 Benzyna 75 Kompakt Granatowy 19993. Selekcja danych
Do dalszego przetwarzania danych i ewlauacji modelu można wyrzucić kolumnę z indeksami krotek
autka_dt <- subset(autka_dt,select = -1)
head(autka_dt)## Marka_pojazdu Model_pojazdu Rok_produkcji Przebieg Pojemność_skokowa
## 1 Volkswagen Polo 2008 181000 1390
## 2 Kia Ceed 2008 179000 1582
## 3 Toyota Land Cruiser 2022 2000 2755
## 4 Fiat 500X 2015 89024 1368
## 5 Toyota Prius 2009 289820 1798
## 6 Ford S-Max 2007 345000 1753
## Rodzaj_paliwa Moc Typ_nadwozia Kolor Cena
## 1 Benzyna 80 Auta miejskie Czarny 15999
## 2 Diesel 115 Kombi Szary 13500
## 3 Diesel 204 SUV Beżowy 287000
## 4 Benzyna 140 Auta miejskie Inny kolor 62730
## 5 Hybryda 99 Kompakt Szary 40500
## 6 Diesel 125 Minivan Srebrny 176004.Transformacja Danych
Na wcześniejszym etapie przetwarzania zbioru nadano odpowiednim cechom typy wartości numeryczne i kategoryczne.
str(autka_dt)## 'data.frame': 1248 obs. of 10 variables:
## $ Marka_pojazdu : Factor w/ 43 levels "Alfa Romeo","Audi",..: 42 19 41 12 41 13 2 3 43 33 ...
## $ Model_pojazdu : Factor w/ 326 levels "107","159","2",..: 214 74 178 20 215 234 223 250 295 71 ...
## $ Rok_produkcji : int 2008 2008 2022 2015 2009 2007 2014 2017 2008 2015 ...
## $ Przebieg : num 181000 179000 2000 89024 289820 ...
## $ Pojemność_skokowa: num 1390 1582 2755 1368 1798 ...
## $ Rodzaj_paliwa : Factor w/ 5 levels "Benzyna","Benzyna+LPG",..: 1 3 3 1 5 3 1 3 3 1 ...
## $ Moc : num 80 115 204 140 99 125 272 190 110 90 ...
## $ Typ_nadwozia : Factor w/ 9 levels "Auta małe","Auta miejskie",..: 2 5 9 2 6 7 9 8 5 9 ...
## $ Kolor : Factor w/ 17 levels "Beżowy","Biały",..: 6 14 1 10 14 13 13 9 10 7 ...
## $ Cena : num 15999 13500 287000 62730 40500 ...Kolumny “Marka Pojazdu” i “Model Pojazdu” są ze spobą powiązane w ten sposób, że wartości z kolumny “Model” należą do podzbioru który można oznaczyć wartościa z kolumny “Marka” np. “Astra”, “Corsa” należą do podzbioru “Opel”. W celu uniknięcia potencjalnych redundancji złączymy obie kolumny, by zachować osobność kategorii i usuniemy “Markę” i “Model”.
autka_dt_MM <- autka_dt
autka_dt_MM$Marka_Model <- paste(autka_dt_MM$Marka_pojazdu,autka_dt$Model_pojazdu)
autka_dt_MM <- subset(autka_dt_MM,select = -1)
autka_dt_MM <- subset(autka_dt_MM,select = -1)
head(autka_dt_MM)## Rok_produkcji Przebieg Pojemność_skokowa Rodzaj_paliwa Moc Typ_nadwozia
## 1 2008 181000 1390 Benzyna 80 Auta miejskie
## 2 2008 179000 1582 Diesel 115 Kombi
## 3 2022 2000 2755 Diesel 204 SUV
## 4 2015 89024 1368 Benzyna 140 Auta miejskie
## 5 2009 289820 1798 Hybryda 99 Kompakt
## 6 2007 345000 1753 Diesel 125 Minivan
## Kolor Cena Marka_Model
## 1 Czarny 15999 Volkswagen Polo
## 2 Szary 13500 Kia Ceed
## 3 Beżowy 287000 Toyota Land Cruiser
## 4 Inny kolor 62730 Fiat 500X
## 5 Szary 40500 Toyota Prius
## 6 Srebrny 17600 Ford S-Maxhead(autka_dt)## Marka_pojazdu Model_pojazdu Rok_produkcji Przebieg Pojemność_skokowa
## 1 Volkswagen Polo 2008 181000 1390
## 2 Kia Ceed 2008 179000 1582
## 3 Toyota Land Cruiser 2022 2000 2755
## 4 Fiat 500X 2015 89024 1368
## 5 Toyota Prius 2009 289820 1798
## 6 Ford S-Max 2007 345000 1753
## Rodzaj_paliwa Moc Typ_nadwozia Kolor Cena
## 1 Benzyna 80 Auta miejskie Czarny 15999
## 2 Diesel 115 Kombi Szary 13500
## 3 Diesel 204 SUV Beżowy 287000
## 4 Benzyna 140 Auta miejskie Inny kolor 62730
## 5 Hybryda 99 Kompakt Szary 40500
## 6 Diesel 125 Minivan Srebrny 17600Wartości kategoryczne zamieniamy na wartości one-hot
library(fastDummies)## Thank you for using fastDummies!## To acknowledge our work, please cite the package:## Kaplan, J. & Schlegel, B. (2023). fastDummies: Fast Creation of Dummy (Binary) Columns and Rows from Categorical Variables. Version 1.7.1. URL: https://github.com/jacobkap/fastDummies, https://jacobkap.github.io/fastDummies/.autka_dt <- dummy_cols(autka_dt,select_columns = c("Marka_pojazdu","Model_pojazdu","Rodzaj_paliwa","Typ_nadwozia","Kolor"),remove_selected_columns = TRUE)
autka_dt_MM <- dummy_cols(autka_dt_MM,select_columns = c("Marka_Model","Rodzaj_paliwa","Typ_nadwozia","Kolor"),remove_selected_columns = TRUE)head(autka_dt)## Rok_produkcji Przebieg Pojemność_skokowa Moc Cena Marka_pojazdu_Alfa Romeo
## 1 2008 181000 1390 80 15999 0
## 2 2008 179000 1582 115 13500 0
## 3 2022 2000 2755 204 287000 0
## 4 2015 89024 1368 140 62730 0
## 5 2009 289820 1798 99 40500 0
## 6 2007 345000 1753 125 17600 0
## Marka_pojazdu_Audi Marka_pojazdu_BMW Marka_pojazdu_Cadillac
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_pojazdu_Chevrolet Marka_pojazdu_Chrysler Marka_pojazdu_Citroën
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_pojazdu_Dacia Marka_pojazdu_Daihatsu Marka_pojazdu_Dodge
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_pojazdu_Ferrari Marka_pojazdu_Fiat Marka_pojazdu_Ford
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 1 0
## 5 0 0 0
## 6 0 0 1
## Marka_pojazdu_Honda Marka_pojazdu_Hyundai Marka_pojazdu_Infiniti
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_pojazdu_Jaguar Marka_pojazdu_Jeep Marka_pojazdu_Kia Marka_pojazdu_Lada
## 1 0 0 0 0
## 2 0 0 1 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Marka_pojazdu_Lamborghini Marka_pojazdu_Lancia Marka_pojazdu_Land Rover
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_pojazdu_Lexus Marka_pojazdu_Mazda Marka_pojazdu_Mercedes-Benz
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_pojazdu_MINI Marka_pojazdu_Mitsubishi Marka_pojazdu_Nissan
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_pojazdu_Opel Marka_pojazdu_Peugeot Marka_pojazdu_Porsche
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_pojazdu_Renault Marka_pojazdu_Saab Marka_pojazdu_Seat
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_pojazdu_Skoda Marka_pojazdu_Smart Marka_pojazdu_SsangYong
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_pojazdu_Subaru Marka_pojazdu_Suzuki Marka_pojazdu_Toyota
## 1 0 0 0
## 2 0 0 0
## 3 0 0 1
## 4 0 0 0
## 5 0 0 1
## 6 0 0 0
## Marka_pojazdu_Volkswagen Marka_pojazdu_Volvo Model_pojazdu_107
## 1 1 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_159 Model_pojazdu_2 Model_pojazdu_2008 Model_pojazdu_207
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_208 Model_pojazdu_2104 Model_pojazdu_3 Model_pojazdu_3008
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_300C Model_pojazdu_300s Model_pojazdu_301 Model_pojazdu_307
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_308 Model_pojazdu_370 Z Model_pojazdu_3GT Model_pojazdu_407
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_5 Model_pojazdu_500 Model_pojazdu_500X Model_pojazdu_508
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 1 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_6 Model_pojazdu_626 Model_pojazdu_718 Boxster
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_718 Cayman Model_pojazdu_80 Model_pojazdu_9-3 Model_pojazdu_911
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_A1 Model_pojazdu_A3 Model_pojazdu_A4 Model_pojazdu_A5
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_A6 Model_pojazdu_A6 Allroad Model_pojazdu_A7 Model_pojazdu_A8
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_Accord Model_pojazdu_Adam Model_pojazdu_Altea
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Altea XL Model_pojazdu_AMG GT Model_pojazdu_Antara
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Arteon Model_pojazdu_Astra Model_pojazdu_ASX
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Ateca Model_pojazdu_Auris Model_pojazdu_Avensis
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Aveo Model_pojazdu_Aygo Model_pojazdu_Baleno
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Berlingo Model_pojazdu_BLS Model_pojazdu_Bravo
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_C-HR Model_pojazdu_C-MAX Model_pojazdu_C1 Model_pojazdu_C3
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_C3 Picasso Model_pojazdu_C30 Model_pojazdu_C4
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_C4 Cactus Model_pojazdu_C4 Grand Picasso
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Model_pojazdu_C4 Picasso Model_pojazdu_C5 Model_pojazdu_C5 Aircross
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_C6 Model_pojazdu_C8 Model_pojazdu_Camry Model_pojazdu_Captiva
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_Captur Model_pojazdu_Carens Model_pojazdu_Cayenne
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Ceed Model_pojazdu_Challenger Model_pojazdu_Charger
## 1 0 0 0
## 2 1 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Cherokee Model_pojazdu_City Model_pojazdu_Civic
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_CL Model_pojazdu_CLA Model_pojazdu_Clio Model_pojazdu_CLS
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_Clubman Model_pojazdu_Colt Model_pojazdu_Combo
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Compass Model_pojazdu_Cooper Model_pojazdu_Cooper S
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Corolla Model_pojazdu_Corsa Model_pojazdu_Countryman
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_CR-V Model_pojazdu_Crafter Model_pojazdu_Crossland X
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Cruze Model_pojazdu_CTS Model_pojazdu_CX Model_pojazdu_CX-3
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_CX-5 Model_pojazdu_CX-9 Model_pojazdu_Delta
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Discovery Model_pojazdu_Doblo Model_pojazdu_DS4
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_DS5 Model_pojazdu_Duster Model_pojazdu_EcoSport
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_EDGE Model_pojazdu_Elantra Model_pojazdu_EQC Model_pojazdu_EQS
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_Escape Model_pojazdu_Espace Model_pojazdu_Exeo
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Expert Model_pojazdu_F150 Model_pojazdu_Fabia
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Fiesta Model_pojazdu_Focus Model_pojazdu_Focus C-Max
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Forester Model_pojazdu_Forfour Model_pojazdu_Freemont
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Fusion Model_pojazdu_FX Model_pojazdu_Galaxy
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Garbus Model_pojazdu_Giulia Model_pojazdu_Giulietta
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_GL Model_pojazdu_GLA Model_pojazdu_GLC Model_pojazdu_GLE
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_GLK Model_pojazdu_Golf Model_pojazdu_Golf Plus
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Golf Sportsvan Model_pojazdu_Grand Cherokee
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Model_pojazdu_Grand Espace Model_pojazdu_Grand Scenic
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Model_pojazdu_Grand Vitara Model_pojazdu_Grand Voyager
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Model_pojazdu_Grande Punto Model_pojazdu_Grandis Model_pojazdu_Grandland
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Grandland X Model_pojazdu_HR-V Model_pojazdu_i10
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_i20 Model_pojazdu_I30 Model_pojazdu_i30 N Model_pojazdu_i40
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_Ibiza Model_pojazdu_Impala Model_pojazdu_Inny
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Insignia Model_pojazdu_IS Model_pojazdu_ix35 Model_pojazdu_Jazz
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_Jetta Model_pojazdu_Journey Model_pojazdu_Juke
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Kadjar Model_pojazdu_Kamiq Model_pojazdu_Kangoo
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Klasa B Model_pojazdu_Klasa C Model_pojazdu_Klasa E
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Klasa S Model_pojazdu_Kodiaq Model_pojazdu_Koleos
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Kona Model_pojazdu_Korando Model_pojazdu_Kuga
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Laguna Model_pojazdu_Lancer Model_pojazdu_Land Cruiser
## 1 0 0 0
## 2 0 0 0
## 3 0 0 1
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Legacy Model_pojazdu_Leon Model_pojazdu_Liana
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Liberty Model_pojazdu_Linea Model_pojazdu_Lodgy
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Logan Model_pojazdu_LX Model_pojazdu_Macan
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Magentis Model_pojazdu_Matrix Model_pojazdu_Megane
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Meriva Model_pojazdu_Micra Model_pojazdu_Mito
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Modus Model_pojazdu_Mokka Model_pojazdu_Mondeo
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Murcielago Model_pojazdu_Mustang Model_pojazdu_Navara
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_New Beetle Model_pojazdu_Niro Model_pojazdu_Note
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_NX Model_pojazdu_Octavia Model_pojazdu_ONE
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Outback Model_pojazdu_Outlander Model_pojazdu_Pacifica
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Panda Model_pojazdu_Partner Model_pojazdu_Passat
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Patrol Model_pojazdu_Picanto Model_pojazdu_Polo
## 1 0 0 1
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Prius Model_pojazdu_Prius+ Model_pojazdu_Pro_cee'd
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 1 0 0
## 6 0 0 0
## Model_pojazdu_ProAce Model_pojazdu_Punto Model_pojazdu_Punto 2012
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Q3 Model_pojazdu_Q5 Model_pojazdu_Q7 Model_pojazdu_Q8
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_Qashqai Model_pojazdu_Qashqai+2 Model_pojazdu_Qubo
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_QX70 Model_pojazdu_Range Rover Sport Model_pojazdu_RAPID
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_RAV4 Model_pojazdu_Rifter Model_pojazdu_Rio Model_pojazdu_S-Max
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 1
## Model_pojazdu_S3 Model_pojazdu_S40 Model_pojazdu_S60 Model_pojazdu_S80
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_Sandero Model_pojazdu_Santa Fe Model_pojazdu_Scala
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Scenic Model_pojazdu_Scirocco Model_pojazdu_Scudo
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Sedici Model_pojazdu_Seria 1 Model_pojazdu_Seria 2
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Seria 3 Model_pojazdu_Seria 4 Model_pojazdu_Seria 5
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Seria 6 Model_pojazdu_Seria 7 Model_pojazdu_Seria 8
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Seria 900 Model_pojazdu_Seria B Model_pojazdu_Sharan
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Sierra Model_pojazdu_Signum Model_pojazdu_Sirion
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_SL Model_pojazdu_SLK Model_pojazdu_Sonata Model_pojazdu_Sorento
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_Soul Model_pojazdu_Space Star Model_pojazdu_Sportage
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Stelvio Model_pojazdu_Stonic Model_pojazdu_Superb
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Swift Model_pojazdu_SX4 Model_pojazdu_SX4 S-Cross
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_T-Roc Model_pojazdu_Talisman Model_pojazdu_Tercel
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Terios Model_pojazdu_Testarossa Model_pojazdu_Tigra
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Tiguan Model_pojazdu_Tipo Model_pojazdu_Toledo
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Tonale Model_pojazdu_Touareg Model_pojazdu_Touran
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Tourneo Courier Model_pojazdu_Town & Country
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Model_pojazdu_Trafic Model_pojazdu_Transit Model_pojazdu_Transporter
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Trax Model_pojazdu_Tribeca Model_pojazdu_TT
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Tucson Model_pojazdu_V40 Model_pojazdu_V50 Model_pojazdu_V60
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_V70 Model_pojazdu_V90 Cross Country Model_pojazdu_Vectra
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Venga Model_pojazdu_Verso Model_pojazdu_Vitara
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Vito Model_pojazdu_Vivaro Model_pojazdu_W124 (1984-1993)
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Wrangler Model_pojazdu_X-Trail Model_pojazdu_X1
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_X2 Model_pojazdu_X3 Model_pojazdu_X4 Model_pojazdu_X5
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_X5 M Model_pojazdu_X6 Model_pojazdu_XC 40 Model_pojazdu_XC 60
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_XC 70 Model_pojazdu_XC 90 Model_pojazdu_XCeed Model_pojazdu_XE
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_XF Model_pojazdu_XL7 Model_pojazdu_Xsara Picasso
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Yaris Model_pojazdu_Zafira Model_pojazdu_Zoe
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Rodzaj_paliwa_Benzyna Rodzaj_paliwa_Benzyna+LPG Rodzaj_paliwa_Diesel
## 1 1 0 0
## 2 0 0 1
## 3 0 0 1
## 4 1 0 0
## 5 0 0 0
## 6 0 0 1
## Rodzaj_paliwa_Elektryczny Rodzaj_paliwa_Hybryda Typ_nadwozia_Auta małe
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 1 0
## 6 0 0 0
## Typ_nadwozia_Auta miejskie Typ_nadwozia_Coupe Typ_nadwozia_Kabriolet
## 1 1 0 0
## 2 0 0 0
## 3 0 0 0
## 4 1 0 0
## 5 0 0 0
## 6 0 0 0
## Typ_nadwozia_Kombi Typ_nadwozia_Kompakt Typ_nadwozia_Minivan
## 1 0 0 0
## 2 1 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 1 0
## 6 0 0 1
## Typ_nadwozia_Sedan Typ_nadwozia_SUV Kolor_Beżowy Kolor_Biały Kolor_Błękitny
## 1 0 0 0 0 0
## 2 0 0 0 0 0
## 3 0 1 1 0 0
## 4 0 0 0 0 0
## 5 0 0 0 0 0
## 6 0 0 0 0 0
## Kolor_Bordowy Kolor_Brązowy Kolor_Czarny Kolor_Czerwony Kolor_Fioletowy
## 1 0 0 1 0 0
## 2 0 0 0 0 0
## 3 0 0 0 0 0
## 4 0 0 0 0 0
## 5 0 0 0 0 0
## 6 0 0 0 0 0
## Kolor_Granatowy Kolor_Inny kolor Kolor_Niebieski Kolor_Pomarańczowy
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 1 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Kolor_Srebrny Kolor_Szary Kolor_Zielony Kolor_Złoty Kolor_Żółty
## 1 0 0 0 0 0
## 2 0 1 0 0 0
## 3 0 0 0 0 0
## 4 0 0 0 0 0
## 5 0 1 0 0 0
## 6 1 0 0 0 0head(autka_dt_MM)## Rok_produkcji Przebieg Pojemność_skokowa Moc Cena
## 1 2008 181000 1390 80 15999
## 2 2008 179000 1582 115 13500
## 3 2022 2000 2755 204 287000
## 4 2015 89024 1368 140 62730
## 5 2009 289820 1798 99 40500
## 6 2007 345000 1753 125 17600
## Marka_Model_Alfa Romeo 159 Marka_Model_Alfa Romeo Giulia
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Alfa Romeo Giulietta Marka_Model_Alfa Romeo Mito
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Alfa Romeo Stelvio Marka_Model_Alfa Romeo Tonale
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Audi 80 Marka_Model_Audi A1 Marka_Model_Audi A3
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Audi A4 Marka_Model_Audi A5 Marka_Model_Audi A6
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Audi A6 Allroad Marka_Model_Audi A7 Marka_Model_Audi A8
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Audi Q3 Marka_Model_Audi Q5 Marka_Model_Audi Q7
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Audi Q8 Marka_Model_Audi S3 Marka_Model_Audi TT
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_BMW 3GT Marka_Model_BMW Seria 1 Marka_Model_BMW Seria 2
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_BMW Seria 3 Marka_Model_BMW Seria 4 Marka_Model_BMW Seria 5
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_BMW Seria 6 Marka_Model_BMW Seria 7 Marka_Model_BMW Seria 8
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_BMW X1 Marka_Model_BMW X2 Marka_Model_BMW X3 Marka_Model_BMW X4
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Marka_Model_BMW X5 Marka_Model_BMW X5 M Marka_Model_BMW X6
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Cadillac BLS Marka_Model_Cadillac CTS Marka_Model_Chevrolet Aveo
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Chevrolet Captiva Marka_Model_Chevrolet Cruze
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Chevrolet Impala Marka_Model_Chevrolet Trax
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Chrysler 300C Marka_Model_Chrysler 300s
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Chrysler Grand Voyager Marka_Model_Chrysler Pacifica
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Chrysler Town & Country Marka_Model_Citroën Berlingo
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Citroën C1 Marka_Model_Citroën C3 Marka_Model_Citroën C3 Picasso
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Citroën C4 Marka_Model_Citroën C4 Cactus
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Citroën C4 Grand Picasso Marka_Model_Citroën C4 Picasso
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Citroën C5 Marka_Model_Citroën C5 Aircross Marka_Model_Citroën C6
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Citroën C8 Marka_Model_Citroën CX Marka_Model_Citroën DS4
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Citroën DS5 Marka_Model_Citroën Xsara Picasso
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Dacia Duster Marka_Model_Dacia Lodgy Marka_Model_Dacia Logan
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Dacia Sandero Marka_Model_Daihatsu Sirion
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Daihatsu Terios Marka_Model_Dodge Challenger
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Dodge Charger Marka_Model_Dodge Journey
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Ferrari Testarossa Marka_Model_Fiat 500 Marka_Model_Fiat 500X
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 1
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Fiat Bravo Marka_Model_Fiat Doblo Marka_Model_Fiat Freemont
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Fiat Grande Punto Marka_Model_Fiat Inny Marka_Model_Fiat Linea
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Fiat Panda Marka_Model_Fiat Punto Marka_Model_Fiat Punto 2012
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Fiat Qubo Marka_Model_Fiat Scudo Marka_Model_Fiat Sedici
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Fiat Tipo Marka_Model_Ford C-MAX Marka_Model_Ford EcoSport
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Ford EDGE Marka_Model_Ford Escape Marka_Model_Ford F150
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Ford Fiesta Marka_Model_Ford Focus Marka_Model_Ford Focus C-Max
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Ford Fusion Marka_Model_Ford Galaxy Marka_Model_Ford Kuga
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Ford Mondeo Marka_Model_Ford Mustang Marka_Model_Ford S-Max
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 1
## Marka_Model_Ford Sierra Marka_Model_Ford Tourneo Courier
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Ford Transit Marka_Model_Honda Accord Marka_Model_Honda City
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Honda Civic Marka_Model_Honda CR-V Marka_Model_Honda HR-V
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Honda Jazz Marka_Model_Hyundai Elantra Marka_Model_Hyundai i10
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Hyundai i20 Marka_Model_Hyundai I30 Marka_Model_Hyundai i30 N
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Hyundai i40 Marka_Model_Hyundai ix35 Marka_Model_Hyundai Kona
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Hyundai Matrix Marka_Model_Hyundai Santa Fe
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Hyundai Sonata Marka_Model_Hyundai Tucson Marka_Model_Infiniti FX
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Infiniti QX70 Marka_Model_Jaguar XE Marka_Model_Jaguar XF
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Jeep Cherokee Marka_Model_Jeep Compass
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Jeep Grand Cherokee Marka_Model_Jeep Liberty
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Jeep Wrangler Marka_Model_Kia Carens Marka_Model_Kia Ceed
## 1 0 0 0
## 2 0 0 1
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Kia Magentis Marka_Model_Kia Niro Marka_Model_Kia Picanto
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Kia Pro_cee'd Marka_Model_Kia Rio Marka_Model_Kia Sorento
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Kia Soul Marka_Model_Kia Sportage Marka_Model_Kia Stonic
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Kia Venga Marka_Model_Kia XCeed Marka_Model_Lada 2104
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Lamborghini Murcielago Marka_Model_Lancia Delta
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Land Rover Discovery Marka_Model_Land Rover Range Rover Sport
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Lexus IS Marka_Model_Lexus LX Marka_Model_Lexus NX
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Mazda 2 Marka_Model_Mazda 3 Marka_Model_Mazda 5
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Mazda 6 Marka_Model_Mazda 626 Marka_Model_Mazda CX-3
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Mazda CX-5 Marka_Model_Mazda CX-9 Marka_Model_Mazda Seria B
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Mercedes-Benz AMG GT Marka_Model_Mercedes-Benz CL
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Mercedes-Benz CLA Marka_Model_Mercedes-Benz CLS
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Mercedes-Benz EQC Marka_Model_Mercedes-Benz EQS
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Mercedes-Benz GL Marka_Model_Mercedes-Benz GLA
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Mercedes-Benz GLC Marka_Model_Mercedes-Benz GLE
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Mercedes-Benz GLK Marka_Model_Mercedes-Benz Klasa B
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Mercedes-Benz Klasa C Marka_Model_Mercedes-Benz Klasa E
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Mercedes-Benz Klasa S Marka_Model_Mercedes-Benz SL
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Mercedes-Benz SLK Marka_Model_Mercedes-Benz Vito
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Mercedes-Benz W124 (1984-1993) Marka_Model_MINI Clubman
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_MINI Cooper Marka_Model_MINI Cooper S Marka_Model_MINI Countryman
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_MINI ONE Marka_Model_Mitsubishi ASX Marka_Model_Mitsubishi Colt
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Mitsubishi Grandis Marka_Model_Mitsubishi Lancer
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Mitsubishi Outlander Marka_Model_Mitsubishi Space Star
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Nissan 370 Z Marka_Model_Nissan Juke Marka_Model_Nissan Micra
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Nissan Navara Marka_Model_Nissan Note Marka_Model_Nissan Patrol
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Nissan Qashqai Marka_Model_Nissan Qashqai+2
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Nissan X-Trail Marka_Model_Opel Adam Marka_Model_Opel Antara
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Opel Astra Marka_Model_Opel Combo Marka_Model_Opel Corsa
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Opel Crossland X Marka_Model_Opel Grandland
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Opel Grandland X Marka_Model_Opel Insignia
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Opel Meriva Marka_Model_Opel Mokka Marka_Model_Opel Signum
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Opel Tigra Marka_Model_Opel Vectra Marka_Model_Opel Vivaro
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Opel Zafira Marka_Model_Peugeot 107 Marka_Model_Peugeot 207
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Peugeot 208 Marka_Model_Peugeot 301 Marka_Model_Peugeot 307
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Peugeot 308 Marka_Model_Peugeot 407 Marka_Model_Peugeot 508
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Peugeot 2008 Marka_Model_Peugeot 3008 Marka_Model_Peugeot Expert
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Peugeot Partner Marka_Model_Peugeot Rifter
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Porsche 718 Boxster Marka_Model_Porsche 718 Cayman
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Porsche 911 Marka_Model_Porsche Cayenne Marka_Model_Porsche Macan
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Renault Captur Marka_Model_Renault Clio
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Renault Espace Marka_Model_Renault Grand Espace
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Renault Grand Scenic Marka_Model_Renault Kadjar
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Renault Kangoo Marka_Model_Renault Koleos
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Renault Laguna Marka_Model_Renault Megane
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Renault Modus Marka_Model_Renault Scenic
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Renault Talisman Marka_Model_Renault Trafic
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Renault Zoe Marka_Model_Saab 9-3 Marka_Model_Seat Altea
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Seat Altea XL Marka_Model_Seat Ateca Marka_Model_Seat Exeo
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Seat Ibiza Marka_Model_Seat Leon Marka_Model_Seat Toledo
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Skoda Fabia Marka_Model_Skoda Kamiq Marka_Model_Skoda Kodiaq
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Skoda Octavia Marka_Model_Skoda RAPID Marka_Model_Skoda Scala
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Skoda Superb Marka_Model_Smart Forfour
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_SsangYong Korando Marka_Model_Subaru Forester
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Subaru Legacy Marka_Model_Subaru Outback
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Subaru Tribeca Marka_Model_Suzuki Baleno
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Suzuki Grand Vitara Marka_Model_Suzuki Liana
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Suzuki Swift Marka_Model_Suzuki SX4
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Suzuki SX4 S-Cross Marka_Model_Suzuki Vitara
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Suzuki XL7 Marka_Model_Toyota Auris Marka_Model_Toyota Avensis
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Toyota Aygo Marka_Model_Toyota C-HR Marka_Model_Toyota Camry
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Toyota Corolla Marka_Model_Toyota Land Cruiser
## 1 0 0
## 2 0 0
## 3 0 1
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Toyota Prius Marka_Model_Toyota Prius+ Marka_Model_Toyota ProAce
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 1 0 0
## 6 0 0 0
## Marka_Model_Toyota RAV4 Marka_Model_Toyota Tercel Marka_Model_Toyota Verso
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Toyota Yaris Marka_Model_Volkswagen Arteon
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Volkswagen Crafter Marka_Model_Volkswagen Garbus
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Volkswagen Golf Marka_Model_Volkswagen Golf Plus
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Volkswagen Golf Sportsvan Marka_Model_Volkswagen Jetta
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Volkswagen New Beetle Marka_Model_Volkswagen Passat
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Volkswagen Polo Marka_Model_Volkswagen Scirocco
## 1 1 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Volkswagen Sharan Marka_Model_Volkswagen T-Roc
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Volkswagen Tiguan Marka_Model_Volkswagen Touareg
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Volkswagen Touran Marka_Model_Volkswagen Transporter
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Volvo C30 Marka_Model_Volvo S40 Marka_Model_Volvo S60
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Volvo S80 Marka_Model_Volvo Seria 900 Marka_Model_Volvo V40
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Volvo V50 Marka_Model_Volvo V60 Marka_Model_Volvo V70
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Volvo V90 Cross Country Marka_Model_Volvo XC 40
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Volvo XC 60 Marka_Model_Volvo XC 70 Marka_Model_Volvo XC 90
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Rodzaj_paliwa_Benzyna Rodzaj_paliwa_Benzyna+LPG Rodzaj_paliwa_Diesel
## 1 1 0 0
## 2 0 0 1
## 3 0 0 1
## 4 1 0 0
## 5 0 0 0
## 6 0 0 1
## Rodzaj_paliwa_Elektryczny Rodzaj_paliwa_Hybryda Typ_nadwozia_Auta małe
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 1 0
## 6 0 0 0
## Typ_nadwozia_Auta miejskie Typ_nadwozia_Coupe Typ_nadwozia_Kabriolet
## 1 1 0 0
## 2 0 0 0
## 3 0 0 0
## 4 1 0 0
## 5 0 0 0
## 6 0 0 0
## Typ_nadwozia_Kombi Typ_nadwozia_Kompakt Typ_nadwozia_Minivan
## 1 0 0 0
## 2 1 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 1 0
## 6 0 0 1
## Typ_nadwozia_Sedan Typ_nadwozia_SUV Kolor_Beżowy Kolor_Biały Kolor_Błękitny
## 1 0 0 0 0 0
## 2 0 0 0 0 0
## 3 0 1 1 0 0
## 4 0 0 0 0 0
## 5 0 0 0 0 0
## 6 0 0 0 0 0
## Kolor_Bordowy Kolor_Brązowy Kolor_Czarny Kolor_Czerwony Kolor_Fioletowy
## 1 0 0 1 0 0
## 2 0 0 0 0 0
## 3 0 0 0 0 0
## 4 0 0 0 0 0
## 5 0 0 0 0 0
## 6 0 0 0 0 0
## Kolor_Granatowy Kolor_Inny kolor Kolor_Niebieski Kolor_Pomarańczowy
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 1 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Kolor_Srebrny Kolor_Szary Kolor_Zielony Kolor_Złoty Kolor_Żółty
## 1 0 0 0 0 0
## 2 0 1 0 0 0
## 3 0 0 0 0 0
## 4 0 0 0 0 0
## 5 0 1 0 0 0
## 6 1 0 0 0 05. Eksploracja Danych
Zgodnie z przyjętym standardem usuwamy ze zbioru w poszczególnych kolumnach wartości, które nie znajdują się między pierwszym a trzecim kwantylem (Q1 -1.5IQR, Q3+1.5IQR)
Przytnij_kwartyl <- function(nazwa_kolumny,dataset)
{
IQR_n <- IQR(autka_dt[[nazwa_kolumny]])
TOP <- quantile(autka_dt[[nazwa_kolumny]])[[4]] + 1.5*IQR_n
BOTTOM <- quantile(autka_dt[[nazwa_kolumny]])[[2]] - 1.5*IQR_n
print(TOP)
autka_dt <- autka_dt[(autka_dt[[nazwa_kolumny]] > BOTTOM),]
autka_dt <- autka_dt[(autka_dt[[nazwa_kolumny]] < TOP),]
autka_dt_MM <- autka_dt_MM[(autka_dt_MM[[nazwa_kolumny]] > BOTTOM),]
autka_dt_MM <- autka_dt_MM[(autka_dt_MM[[nazwa_kolumny]] < TOP),]
boxplot(autka_dt[[nazwa_kolumny]])
boxplot(autka_dt_MM[[nazwa_kolumny]])
}Przytnij_kwartyl("Rok_produkcji")## [1] 2029.5
Przytnij_kwartyl("Przebieg")## [1] 440203.9
Przytnij_kwartyl("Pojemność_skokowa")## [1] 2652.5
Przytnij_kwartyl("Moc")## [1] 270.625
Przytnij_kwartyl("Cena")## [1] 128816.2
ggcorrplot(cor(autka_liczbowe2), lab=TRUE, p.mat = cor_pmat(autka_liczbowe2))
Nie powinna dziwić wysoka korelacja między mocą silnika a pojemnością skokową. Może ona jednak być niekorzystna w dalszym przetwarzaniu. Dlatego zbudujemy dwa modele, w których wykorzystamy zbiór danych bez jednej z tych cech.
head(autka_dt)## Rok_produkcji Przebieg Pojemność_skokowa Moc Cena Marka_pojazdu_Alfa Romeo
## 1 2008 181000 1390 80 15999 0
## 2 2008 179000 1582 115 13500 0
## 3 2022 2000 2755 204 287000 0
## 4 2015 89024 1368 140 62730 0
## 5 2009 289820 1798 99 40500 0
## 6 2007 345000 1753 125 17600 0
## Marka_pojazdu_Audi Marka_pojazdu_BMW Marka_pojazdu_Cadillac
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_pojazdu_Chevrolet Marka_pojazdu_Chrysler Marka_pojazdu_Citroën
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_pojazdu_Dacia Marka_pojazdu_Daihatsu Marka_pojazdu_Dodge
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_pojazdu_Ferrari Marka_pojazdu_Fiat Marka_pojazdu_Ford
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 1 0
## 5 0 0 0
## 6 0 0 1
## Marka_pojazdu_Honda Marka_pojazdu_Hyundai Marka_pojazdu_Infiniti
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_pojazdu_Jaguar Marka_pojazdu_Jeep Marka_pojazdu_Kia Marka_pojazdu_Lada
## 1 0 0 0 0
## 2 0 0 1 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Marka_pojazdu_Lamborghini Marka_pojazdu_Lancia Marka_pojazdu_Land Rover
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_pojazdu_Lexus Marka_pojazdu_Mazda Marka_pojazdu_Mercedes-Benz
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_pojazdu_MINI Marka_pojazdu_Mitsubishi Marka_pojazdu_Nissan
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_pojazdu_Opel Marka_pojazdu_Peugeot Marka_pojazdu_Porsche
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_pojazdu_Renault Marka_pojazdu_Saab Marka_pojazdu_Seat
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_pojazdu_Skoda Marka_pojazdu_Smart Marka_pojazdu_SsangYong
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_pojazdu_Subaru Marka_pojazdu_Suzuki Marka_pojazdu_Toyota
## 1 0 0 0
## 2 0 0 0
## 3 0 0 1
## 4 0 0 0
## 5 0 0 1
## 6 0 0 0
## Marka_pojazdu_Volkswagen Marka_pojazdu_Volvo Model_pojazdu_107
## 1 1 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_159 Model_pojazdu_2 Model_pojazdu_2008 Model_pojazdu_207
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_208 Model_pojazdu_2104 Model_pojazdu_3 Model_pojazdu_3008
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_300C Model_pojazdu_300s Model_pojazdu_301 Model_pojazdu_307
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_308 Model_pojazdu_370 Z Model_pojazdu_3GT Model_pojazdu_407
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_5 Model_pojazdu_500 Model_pojazdu_500X Model_pojazdu_508
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 1 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_6 Model_pojazdu_626 Model_pojazdu_718 Boxster
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_718 Cayman Model_pojazdu_80 Model_pojazdu_9-3 Model_pojazdu_911
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_A1 Model_pojazdu_A3 Model_pojazdu_A4 Model_pojazdu_A5
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_A6 Model_pojazdu_A6 Allroad Model_pojazdu_A7 Model_pojazdu_A8
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_Accord Model_pojazdu_Adam Model_pojazdu_Altea
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Altea XL Model_pojazdu_AMG GT Model_pojazdu_Antara
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Arteon Model_pojazdu_Astra Model_pojazdu_ASX
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Ateca Model_pojazdu_Auris Model_pojazdu_Avensis
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Aveo Model_pojazdu_Aygo Model_pojazdu_Baleno
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Berlingo Model_pojazdu_BLS Model_pojazdu_Bravo
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_C-HR Model_pojazdu_C-MAX Model_pojazdu_C1 Model_pojazdu_C3
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_C3 Picasso Model_pojazdu_C30 Model_pojazdu_C4
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_C4 Cactus Model_pojazdu_C4 Grand Picasso
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Model_pojazdu_C4 Picasso Model_pojazdu_C5 Model_pojazdu_C5 Aircross
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_C6 Model_pojazdu_C8 Model_pojazdu_Camry Model_pojazdu_Captiva
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_Captur Model_pojazdu_Carens Model_pojazdu_Cayenne
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Ceed Model_pojazdu_Challenger Model_pojazdu_Charger
## 1 0 0 0
## 2 1 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Cherokee Model_pojazdu_City Model_pojazdu_Civic
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_CL Model_pojazdu_CLA Model_pojazdu_Clio Model_pojazdu_CLS
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_Clubman Model_pojazdu_Colt Model_pojazdu_Combo
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Compass Model_pojazdu_Cooper Model_pojazdu_Cooper S
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Corolla Model_pojazdu_Corsa Model_pojazdu_Countryman
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_CR-V Model_pojazdu_Crafter Model_pojazdu_Crossland X
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Cruze Model_pojazdu_CTS Model_pojazdu_CX Model_pojazdu_CX-3
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_CX-5 Model_pojazdu_CX-9 Model_pojazdu_Delta
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Discovery Model_pojazdu_Doblo Model_pojazdu_DS4
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_DS5 Model_pojazdu_Duster Model_pojazdu_EcoSport
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_EDGE Model_pojazdu_Elantra Model_pojazdu_EQC Model_pojazdu_EQS
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_Escape Model_pojazdu_Espace Model_pojazdu_Exeo
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Expert Model_pojazdu_F150 Model_pojazdu_Fabia
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Fiesta Model_pojazdu_Focus Model_pojazdu_Focus C-Max
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Forester Model_pojazdu_Forfour Model_pojazdu_Freemont
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Fusion Model_pojazdu_FX Model_pojazdu_Galaxy
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Garbus Model_pojazdu_Giulia Model_pojazdu_Giulietta
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_GL Model_pojazdu_GLA Model_pojazdu_GLC Model_pojazdu_GLE
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_GLK Model_pojazdu_Golf Model_pojazdu_Golf Plus
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Golf Sportsvan Model_pojazdu_Grand Cherokee
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Model_pojazdu_Grand Espace Model_pojazdu_Grand Scenic
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Model_pojazdu_Grand Vitara Model_pojazdu_Grand Voyager
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Model_pojazdu_Grande Punto Model_pojazdu_Grandis Model_pojazdu_Grandland
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Grandland X Model_pojazdu_HR-V Model_pojazdu_i10
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_i20 Model_pojazdu_I30 Model_pojazdu_i30 N Model_pojazdu_i40
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_Ibiza Model_pojazdu_Impala Model_pojazdu_Inny
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Insignia Model_pojazdu_IS Model_pojazdu_ix35 Model_pojazdu_Jazz
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_Jetta Model_pojazdu_Journey Model_pojazdu_Juke
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Kadjar Model_pojazdu_Kamiq Model_pojazdu_Kangoo
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Klasa B Model_pojazdu_Klasa C Model_pojazdu_Klasa E
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Klasa S Model_pojazdu_Kodiaq Model_pojazdu_Koleos
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Kona Model_pojazdu_Korando Model_pojazdu_Kuga
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Laguna Model_pojazdu_Lancer Model_pojazdu_Land Cruiser
## 1 0 0 0
## 2 0 0 0
## 3 0 0 1
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Legacy Model_pojazdu_Leon Model_pojazdu_Liana
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Liberty Model_pojazdu_Linea Model_pojazdu_Lodgy
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Logan Model_pojazdu_LX Model_pojazdu_Macan
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Magentis Model_pojazdu_Matrix Model_pojazdu_Megane
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Meriva Model_pojazdu_Micra Model_pojazdu_Mito
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Modus Model_pojazdu_Mokka Model_pojazdu_Mondeo
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Murcielago Model_pojazdu_Mustang Model_pojazdu_Navara
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_New Beetle Model_pojazdu_Niro Model_pojazdu_Note
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_NX Model_pojazdu_Octavia Model_pojazdu_ONE
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Outback Model_pojazdu_Outlander Model_pojazdu_Pacifica
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Panda Model_pojazdu_Partner Model_pojazdu_Passat
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Patrol Model_pojazdu_Picanto Model_pojazdu_Polo
## 1 0 0 1
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Prius Model_pojazdu_Prius+ Model_pojazdu_Pro_cee'd
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 1 0 0
## 6 0 0 0
## Model_pojazdu_ProAce Model_pojazdu_Punto Model_pojazdu_Punto 2012
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Q3 Model_pojazdu_Q5 Model_pojazdu_Q7 Model_pojazdu_Q8
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_Qashqai Model_pojazdu_Qashqai+2 Model_pojazdu_Qubo
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_QX70 Model_pojazdu_Range Rover Sport Model_pojazdu_RAPID
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_RAV4 Model_pojazdu_Rifter Model_pojazdu_Rio Model_pojazdu_S-Max
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 1
## Model_pojazdu_S3 Model_pojazdu_S40 Model_pojazdu_S60 Model_pojazdu_S80
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_Sandero Model_pojazdu_Santa Fe Model_pojazdu_Scala
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Scenic Model_pojazdu_Scirocco Model_pojazdu_Scudo
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Sedici Model_pojazdu_Seria 1 Model_pojazdu_Seria 2
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Seria 3 Model_pojazdu_Seria 4 Model_pojazdu_Seria 5
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Seria 6 Model_pojazdu_Seria 7 Model_pojazdu_Seria 8
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Seria 900 Model_pojazdu_Seria B Model_pojazdu_Sharan
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Sierra Model_pojazdu_Signum Model_pojazdu_Sirion
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_SL Model_pojazdu_SLK Model_pojazdu_Sonata Model_pojazdu_Sorento
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_Soul Model_pojazdu_Space Star Model_pojazdu_Sportage
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Stelvio Model_pojazdu_Stonic Model_pojazdu_Superb
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Swift Model_pojazdu_SX4 Model_pojazdu_SX4 S-Cross
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_T-Roc Model_pojazdu_Talisman Model_pojazdu_Tercel
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Terios Model_pojazdu_Testarossa Model_pojazdu_Tigra
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Tiguan Model_pojazdu_Tipo Model_pojazdu_Toledo
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Tonale Model_pojazdu_Touareg Model_pojazdu_Touran
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Tourneo Courier Model_pojazdu_Town & Country
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Model_pojazdu_Trafic Model_pojazdu_Transit Model_pojazdu_Transporter
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Trax Model_pojazdu_Tribeca Model_pojazdu_TT
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Tucson Model_pojazdu_V40 Model_pojazdu_V50 Model_pojazdu_V60
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_V70 Model_pojazdu_V90 Cross Country Model_pojazdu_Vectra
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Venga Model_pojazdu_Verso Model_pojazdu_Vitara
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Vito Model_pojazdu_Vivaro Model_pojazdu_W124 (1984-1993)
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Wrangler Model_pojazdu_X-Trail Model_pojazdu_X1
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_X2 Model_pojazdu_X3 Model_pojazdu_X4 Model_pojazdu_X5
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_X5 M Model_pojazdu_X6 Model_pojazdu_XC 40 Model_pojazdu_XC 60
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_XC 70 Model_pojazdu_XC 90 Model_pojazdu_XCeed Model_pojazdu_XE
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Model_pojazdu_XF Model_pojazdu_XL7 Model_pojazdu_Xsara Picasso
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Model_pojazdu_Yaris Model_pojazdu_Zafira Model_pojazdu_Zoe
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Rodzaj_paliwa_Benzyna Rodzaj_paliwa_Benzyna+LPG Rodzaj_paliwa_Diesel
## 1 1 0 0
## 2 0 0 1
## 3 0 0 1
## 4 1 0 0
## 5 0 0 0
## 6 0 0 1
## Rodzaj_paliwa_Elektryczny Rodzaj_paliwa_Hybryda Typ_nadwozia_Auta małe
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 1 0
## 6 0 0 0
## Typ_nadwozia_Auta miejskie Typ_nadwozia_Coupe Typ_nadwozia_Kabriolet
## 1 1 0 0
## 2 0 0 0
## 3 0 0 0
## 4 1 0 0
## 5 0 0 0
## 6 0 0 0
## Typ_nadwozia_Kombi Typ_nadwozia_Kompakt Typ_nadwozia_Minivan
## 1 0 0 0
## 2 1 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 1 0
## 6 0 0 1
## Typ_nadwozia_Sedan Typ_nadwozia_SUV Kolor_Beżowy Kolor_Biały Kolor_Błękitny
## 1 0 0 0 0 0
## 2 0 0 0 0 0
## 3 0 1 1 0 0
## 4 0 0 0 0 0
## 5 0 0 0 0 0
## 6 0 0 0 0 0
## Kolor_Bordowy Kolor_Brązowy Kolor_Czarny Kolor_Czerwony Kolor_Fioletowy
## 1 0 0 1 0 0
## 2 0 0 0 0 0
## 3 0 0 0 0 0
## 4 0 0 0 0 0
## 5 0 0 0 0 0
## 6 0 0 0 0 0
## Kolor_Granatowy Kolor_Inny kolor Kolor_Niebieski Kolor_Pomarańczowy
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 1 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Kolor_Srebrny Kolor_Szary Kolor_Zielony Kolor_Złoty Kolor_Żółty
## 1 0 0 0 0 0
## 2 0 1 0 0 0
## 3 0 0 0 0 0
## 4 0 0 0 0 0
## 5 0 1 0 0 0
## 6 1 0 0 0 0head(autka_dt_MM)## Rok_produkcji Przebieg Pojemność_skokowa Moc Cena
## 1 2008 181000 1390 80 15999
## 2 2008 179000 1582 115 13500
## 3 2022 2000 2755 204 287000
## 4 2015 89024 1368 140 62730
## 5 2009 289820 1798 99 40500
## 6 2007 345000 1753 125 17600
## Marka_Model_Alfa Romeo 159 Marka_Model_Alfa Romeo Giulia
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Alfa Romeo Giulietta Marka_Model_Alfa Romeo Mito
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Alfa Romeo Stelvio Marka_Model_Alfa Romeo Tonale
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Marka_Model_Audi 80 Marka_Model_Audi A1 Marka_Model_Audi A3
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Audi A4 Marka_Model_Audi A5 Marka_Model_Audi A6
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Audi A6 Allroad Marka_Model_Audi A7 Marka_Model_Audi A8
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Audi Q3 Marka_Model_Audi Q5 Marka_Model_Audi Q7
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Audi Q8 Marka_Model_Audi S3 Marka_Model_Audi TT
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_BMW 3GT Marka_Model_BMW Seria 1 Marka_Model_BMW Seria 2
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_BMW Seria 3 Marka_Model_BMW Seria 4 Marka_Model_BMW Seria 5
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_BMW Seria 6 Marka_Model_BMW Seria 7 Marka_Model_BMW Seria 8
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_BMW X1 Marka_Model_BMW X2 Marka_Model_BMW X3 Marka_Model_BMW X4
## 1 0 0 0 0
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## Marka_Model_Cadillac BLS Marka_Model_Cadillac CTS Marka_Model_Chevrolet Aveo
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## Marka_Model_Chevrolet Captiva Marka_Model_Chevrolet Cruze
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## Marka_Model_Chevrolet Impala Marka_Model_Chevrolet Trax
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## Marka_Model_Chrysler 300C Marka_Model_Chrysler 300s
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## Marka_Model_Chrysler Grand Voyager Marka_Model_Chrysler Pacifica
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## Marka_Model_Chrysler Town & Country Marka_Model_Citroën Berlingo
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## Marka_Model_Citroën C1 Marka_Model_Citroën C3 Marka_Model_Citroën C3 Picasso
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## Marka_Model_Citroën C4 Marka_Model_Citroën C4 Cactus
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## Marka_Model_Citroën C4 Grand Picasso Marka_Model_Citroën C4 Picasso
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## Marka_Model_Citroën C5 Marka_Model_Citroën C5 Aircross Marka_Model_Citroën C6
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## Marka_Model_Citroën C8 Marka_Model_Citroën CX Marka_Model_Citroën DS4
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## Marka_Model_Citroën DS5 Marka_Model_Citroën Xsara Picasso
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## Marka_Model_Dacia Duster Marka_Model_Dacia Lodgy Marka_Model_Dacia Logan
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## Marka_Model_Dacia Sandero Marka_Model_Daihatsu Sirion
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## Marka_Model_Daihatsu Terios Marka_Model_Dodge Challenger
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## Marka_Model_Dodge Charger Marka_Model_Dodge Journey
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## Marka_Model_Ferrari Testarossa Marka_Model_Fiat 500 Marka_Model_Fiat 500X
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## 6 0 0 0
## Marka_Model_Fiat Bravo Marka_Model_Fiat Doblo Marka_Model_Fiat Freemont
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## 6 0 0 0
## Marka_Model_Fiat Grande Punto Marka_Model_Fiat Inny Marka_Model_Fiat Linea
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## Marka_Model_Fiat Panda Marka_Model_Fiat Punto Marka_Model_Fiat Punto 2012
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## Marka_Model_Fiat Qubo Marka_Model_Fiat Scudo Marka_Model_Fiat Sedici
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## Marka_Model_Fiat Tipo Marka_Model_Ford C-MAX Marka_Model_Ford EcoSport
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## Marka_Model_Ford EDGE Marka_Model_Ford Escape Marka_Model_Ford F150
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## Marka_Model_Ford Fiesta Marka_Model_Ford Focus Marka_Model_Ford Focus C-Max
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## Marka_Model_Ford Fusion Marka_Model_Ford Galaxy Marka_Model_Ford Kuga
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## Marka_Model_Ford Mondeo Marka_Model_Ford Mustang Marka_Model_Ford S-Max
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## Marka_Model_Ford Sierra Marka_Model_Ford Tourneo Courier
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## Marka_Model_Ford Transit Marka_Model_Honda Accord Marka_Model_Honda City
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## Marka_Model_Honda Civic Marka_Model_Honda CR-V Marka_Model_Honda HR-V
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## Marka_Model_Honda Jazz Marka_Model_Hyundai Elantra Marka_Model_Hyundai i10
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## Marka_Model_Hyundai i20 Marka_Model_Hyundai I30 Marka_Model_Hyundai i30 N
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## Marka_Model_Hyundai i40 Marka_Model_Hyundai ix35 Marka_Model_Hyundai Kona
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## Marka_Model_Hyundai Matrix Marka_Model_Hyundai Santa Fe
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## 3 0 0
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## Marka_Model_Hyundai Sonata Marka_Model_Hyundai Tucson Marka_Model_Infiniti FX
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## 6 0 0 0
## Marka_Model_Infiniti QX70 Marka_Model_Jaguar XE Marka_Model_Jaguar XF
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## Marka_Model_Jeep Cherokee Marka_Model_Jeep Compass
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## 2 0 0
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## 6 0 0
## Marka_Model_Jeep Grand Cherokee Marka_Model_Jeep Liberty
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## 6 0 0
## Marka_Model_Jeep Wrangler Marka_Model_Kia Carens Marka_Model_Kia Ceed
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## 6 0 0 0
## Marka_Model_Kia Magentis Marka_Model_Kia Niro Marka_Model_Kia Picanto
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## 6 0 0 0
## Marka_Model_Kia Pro_cee'd Marka_Model_Kia Rio Marka_Model_Kia Sorento
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## Marka_Model_Kia Soul Marka_Model_Kia Sportage Marka_Model_Kia Stonic
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## Marka_Model_Kia Venga Marka_Model_Kia XCeed Marka_Model_Lada 2104
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## Marka_Model_Lamborghini Murcielago Marka_Model_Lancia Delta
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## Marka_Model_Land Rover Discovery Marka_Model_Land Rover Range Rover Sport
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## 6 0 0
## Marka_Model_Lexus IS Marka_Model_Lexus LX Marka_Model_Lexus NX
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## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Mazda 2 Marka_Model_Mazda 3 Marka_Model_Mazda 5
## 1 0 0 0
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## 3 0 0 0
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## 5 0 0 0
## 6 0 0 0
## Marka_Model_Mazda 6 Marka_Model_Mazda 626 Marka_Model_Mazda CX-3
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Mazda CX-5 Marka_Model_Mazda CX-9 Marka_Model_Mazda Seria B
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## 2 0 0 0
## 3 0 0 0
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## 6 0 0 0
## Marka_Model_Mercedes-Benz AMG GT Marka_Model_Mercedes-Benz CL
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## 2 0 0
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## Marka_Model_Mercedes-Benz CLA Marka_Model_Mercedes-Benz CLS
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## Marka_Model_Mercedes-Benz EQC Marka_Model_Mercedes-Benz EQS
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## 2 0 0
## 3 0 0
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## Marka_Model_Mercedes-Benz GL Marka_Model_Mercedes-Benz GLA
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## 2 0 0
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## Marka_Model_Mercedes-Benz GLC Marka_Model_Mercedes-Benz GLE
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## 2 0 0
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## Marka_Model_Mercedes-Benz GLK Marka_Model_Mercedes-Benz Klasa B
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## Marka_Model_Mercedes-Benz Klasa C Marka_Model_Mercedes-Benz Klasa E
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## Marka_Model_Mercedes-Benz Klasa S Marka_Model_Mercedes-Benz SL
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## Marka_Model_Mercedes-Benz SLK Marka_Model_Mercedes-Benz Vito
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## Marka_Model_Mercedes-Benz W124 (1984-1993) Marka_Model_MINI Clubman
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## Marka_Model_MINI Cooper Marka_Model_MINI Cooper S Marka_Model_MINI Countryman
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## Marka_Model_MINI ONE Marka_Model_Mitsubishi ASX Marka_Model_Mitsubishi Colt
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## Marka_Model_Mitsubishi Grandis Marka_Model_Mitsubishi Lancer
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## Marka_Model_Mitsubishi Outlander Marka_Model_Mitsubishi Space Star
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## Marka_Model_Nissan 370 Z Marka_Model_Nissan Juke Marka_Model_Nissan Micra
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## Marka_Model_Nissan Navara Marka_Model_Nissan Note Marka_Model_Nissan Patrol
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## 5 0 0 0
## 6 0 0 0
## Marka_Model_Nissan Qashqai Marka_Model_Nissan Qashqai+2
## 1 0 0
## 2 0 0
## 3 0 0
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## 5 0 0
## 6 0 0
## Marka_Model_Nissan X-Trail Marka_Model_Opel Adam Marka_Model_Opel Antara
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## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Opel Astra Marka_Model_Opel Combo Marka_Model_Opel Corsa
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
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## 6 0 0 0
## Marka_Model_Opel Crossland X Marka_Model_Opel Grandland
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## 2 0 0
## 3 0 0
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## 5 0 0
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## Marka_Model_Opel Grandland X Marka_Model_Opel Insignia
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## 6 0 0
## Marka_Model_Opel Meriva Marka_Model_Opel Mokka Marka_Model_Opel Signum
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## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Opel Tigra Marka_Model_Opel Vectra Marka_Model_Opel Vivaro
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Opel Zafira Marka_Model_Peugeot 107 Marka_Model_Peugeot 207
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## 3 0 0 0
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## 5 0 0 0
## 6 0 0 0
## Marka_Model_Peugeot 208 Marka_Model_Peugeot 301 Marka_Model_Peugeot 307
## 1 0 0 0
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## Marka_Model_Peugeot 308 Marka_Model_Peugeot 407 Marka_Model_Peugeot 508
## 1 0 0 0
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## Marka_Model_Peugeot 2008 Marka_Model_Peugeot 3008 Marka_Model_Peugeot Expert
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## Marka_Model_Peugeot Partner Marka_Model_Peugeot Rifter
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## Marka_Model_Porsche 718 Boxster Marka_Model_Porsche 718 Cayman
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## Marka_Model_Porsche 911 Marka_Model_Porsche Cayenne Marka_Model_Porsche Macan
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## Marka_Model_Renault Captur Marka_Model_Renault Clio
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## Marka_Model_Renault Espace Marka_Model_Renault Grand Espace
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## Marka_Model_Renault Grand Scenic Marka_Model_Renault Kadjar
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## Marka_Model_Renault Kangoo Marka_Model_Renault Koleos
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## Marka_Model_Renault Laguna Marka_Model_Renault Megane
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## Marka_Model_Renault Modus Marka_Model_Renault Scenic
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## Marka_Model_Renault Talisman Marka_Model_Renault Trafic
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## Marka_Model_Renault Zoe Marka_Model_Saab 9-3 Marka_Model_Seat Altea
## 1 0 0 0
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## 6 0 0 0
## Marka_Model_Seat Altea XL Marka_Model_Seat Ateca Marka_Model_Seat Exeo
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
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## 6 0 0 0
## Marka_Model_Seat Ibiza Marka_Model_Seat Leon Marka_Model_Seat Toledo
## 1 0 0 0
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## Marka_Model_Skoda Fabia Marka_Model_Skoda Kamiq Marka_Model_Skoda Kodiaq
## 1 0 0 0
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## 6 0 0 0
## Marka_Model_Skoda Octavia Marka_Model_Skoda RAPID Marka_Model_Skoda Scala
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## Marka_Model_Skoda Superb Marka_Model_Smart Forfour
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## Marka_Model_SsangYong Korando Marka_Model_Subaru Forester
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## Marka_Model_Subaru Legacy Marka_Model_Subaru Outback
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## Marka_Model_Subaru Tribeca Marka_Model_Suzuki Baleno
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## Marka_Model_Suzuki Grand Vitara Marka_Model_Suzuki Liana
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## Marka_Model_Suzuki Swift Marka_Model_Suzuki SX4
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## Marka_Model_Suzuki SX4 S-Cross Marka_Model_Suzuki Vitara
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## Marka_Model_Suzuki XL7 Marka_Model_Toyota Auris Marka_Model_Toyota Avensis
## 1 0 0 0
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## 6 0 0 0
## Marka_Model_Toyota Aygo Marka_Model_Toyota C-HR Marka_Model_Toyota Camry
## 1 0 0 0
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## 5 0 0 0
## 6 0 0 0
## Marka_Model_Toyota Corolla Marka_Model_Toyota Land Cruiser
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## 3 0 1
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## 5 0 0
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## Marka_Model_Toyota Prius Marka_Model_Toyota Prius+ Marka_Model_Toyota ProAce
## 1 0 0 0
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## 3 0 0 0
## 4 0 0 0
## 5 1 0 0
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## Marka_Model_Toyota RAV4 Marka_Model_Toyota Tercel Marka_Model_Toyota Verso
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## Marka_Model_Toyota Yaris Marka_Model_Volkswagen Arteon
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## Marka_Model_Volkswagen Crafter Marka_Model_Volkswagen Garbus
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## Marka_Model_Volkswagen Golf Marka_Model_Volkswagen Golf Plus
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## Marka_Model_Volkswagen Golf Sportsvan Marka_Model_Volkswagen Jetta
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## Marka_Model_Volkswagen New Beetle Marka_Model_Volkswagen Passat
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## 2 0 0
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## Marka_Model_Volkswagen Polo Marka_Model_Volkswagen Scirocco
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## Marka_Model_Volkswagen Sharan Marka_Model_Volkswagen T-Roc
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## Marka_Model_Volkswagen Tiguan Marka_Model_Volkswagen Touareg
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## Marka_Model_Volkswagen Touran Marka_Model_Volkswagen Transporter
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## Marka_Model_Volvo C30 Marka_Model_Volvo S40 Marka_Model_Volvo S60
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
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## 5 0 0 0
## 6 0 0 0
## Marka_Model_Volvo S80 Marka_Model_Volvo Seria 900 Marka_Model_Volvo V40
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
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## 5 0 0 0
## 6 0 0 0
## Marka_Model_Volvo V50 Marka_Model_Volvo V60 Marka_Model_Volvo V70
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Marka_Model_Volvo V90 Cross Country Marka_Model_Volvo XC 40
## 1 0 0
## 2 0 0
## 3 0 0
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## 5 0 0
## 6 0 0
## Marka_Model_Volvo XC 60 Marka_Model_Volvo XC 70 Marka_Model_Volvo XC 90
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
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## 3 0 0 1
## 4 1 0 0
## 5 0 0 0
## 6 0 0 1
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## 2 0 0 0
## 3 0 0 0
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## 5 0 1 0
## 6 0 0 0
## Typ_nadwozia_Auta miejskie Typ_nadwozia_Coupe Typ_nadwozia_Kabriolet
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## 3 0 0 0
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## Typ_nadwozia_Kombi Typ_nadwozia_Kompakt Typ_nadwozia_Minivan
## 1 0 0 0
## 2 1 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 1 0
## 6 0 0 1
## Typ_nadwozia_Sedan Typ_nadwozia_SUV Kolor_Beżowy Kolor_Biały Kolor_Błękitny
## 1 0 0 0 0 0
## 2 0 0 0 0 0
## 3 0 1 1 0 0
## 4 0 0 0 0 0
## 5 0 0 0 0 0
## 6 0 0 0 0 0
## Kolor_Bordowy Kolor_Brązowy Kolor_Czarny Kolor_Czerwony Kolor_Fioletowy
## 1 0 0 1 0 0
## 2 0 0 0 0 0
## 3 0 0 0 0 0
## 4 0 0 0 0 0
## 5 0 0 0 0 0
## 6 0 0 0 0 0
## Kolor_Granatowy Kolor_Inny kolor Kolor_Niebieski Kolor_Pomarańczowy
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 1 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Kolor_Srebrny Kolor_Szary Kolor_Zielony Kolor_Złoty Kolor_Żółty
## 1 0 0 0 0 0
## 2 0 1 0 0 0
## 3 0 0 0 0 0
## 4 0 0 0 0 0
## 5 0 1 0 0 0
## 6 1 0 0 0 0autka_dt_no_eng_capacity <- subset(autka_dt,select = -3)
autka_dt_no_eng_capacity## Rok_produkcji Przebieg Moc Cena Marka_pojazdu_Alfa Romeo
## 1 2008 181000 80 15999 0
## 2 2008 179000 115 13500 0
## 3 2022 2000 204 287000 0
## 4 2015 89024 140 62730 0
## 5 2009 289820 99 40500 0
## 6 2007 345000 125 17600 0
## 7 2014 250000 272 52900 0
## 8 2017 191000 190 115000 0
## 9 2008 246000 110 15900 0
## 10 2015 80297 90 48000 0
## 11 2010 235000 109 28600 0
## 12 2010 204000 160 34600 0
## 13 2020 10700 83 54900 0
## 14 2006 307000 180 27500 0
## 15 1995 282000 174 19900 0
## 16 2015 78658 73 39900 0
## 17 2016 174047 120 60900 0
## 18 2015 168544 210 84900 0
## 19 2016 73888 160 56000 0
## 20 2013 142891 120 59900 0
## 21 2013 214000 150 66666 0
## 22 2017 62692 90 42500 0
## 23 2016 113000 71 23900 0
## 24 2007 135761 87 18500 0
## 25 2009 180000 143 22000 0
## 26 2019 53500 99 62000 0
## 27 2012 144550 160 48900 0
## 28 2017 39000 111 75000 0
## 29 2004 253200 129 19500 0
## 30 2022 4800 84 69900 0
## 31 2004 201500 60 4500 0
## 32 2002 424590 105 4999 0
## 33 2017 106533 150 81900 0
## 34 2015 110432 115 34900 0
## 35 2013 151562 203 53900 0
## 36 2022 5 160 186900 1
## 37 2013 73500 99 37900 0
## 38 2022 2 110 101800 0
## 39 2001 242500 144 18600 0
## 40 2017 83000 102 49500 0
## 41 2013 297000 140 43850 0
## 42 2018 53094 132 73900 0
## 43 2015 147981 115 69900 0
## 44 2022 8 100 68800 0
## 45 2009 250000 190 26900 0
## 46 2008 120000 136 23900 0
## 47 2008 283714 105 18300 0
## 48 2018 67000 98 108900 0
## 49 2013 143000 136 48900 0
## 50 2011 175000 112 21900 0
## 51 2009 99491 90 16900 0
## 52 2019 140000 170 68000 0
## 53 2009 248737 116 19400 0
## 54 2019 120000 286 99000 0
## 55 2012 201000 163 34800 0
## 56 2019 61625 190 149000 0
## 57 2004 285000 163 24950 0
## 58 1993 240000 184 13900 0
## 59 2005 337000 334 35000 0
## 60 2013 109485 136 39000 0
## 61 2016 52691 125 46000 0
## 62 2009 262957 114 26000 0
## 63 2006 123602 80 14000 0
## 64 2008 233109 130 15000 0
## 65 2011 267162 140 37000 0
## 66 2009 171180 143 29500 0
## 67 2012 235410 86 16900 0
## 68 2014 83150 241 36900 0
## 69 2008 259654 120 12999 1
## 70 2006 271580 306 39999 0
## 71 2013 269756 130 29900 0
## 72 2007 225860 185 39999 0
## 73 2000 318509 334 29999 0
## 74 2015 180000 121 102436 0
## 75 2005 269245 136 22500 0
## 76 2004 222000 163 18900 0
## 77 2004 168000 170 16000 0
## 78 2008 178080 90 14900 0
## 79 2018 65000 125 46400 0
## 80 2016 116500 300 119999 0
## 81 2012 171000 143 53000 0
## 82 1999 131226 125 10000 0
## 83 2012 195000 128 37000 0
## 84 2017 102000 129 71955 0
## 85 2012 205816 163 40700 0
## 86 1997 248500 193 14900 0
## 87 2010 194000 109 19900 0
## 88 2009 350000 147 16000 0
## 89 2007 220000 105 14900 0
## 90 2010 280200 116 19400 0
## 91 2007 242500 177 32888 0
## 92 2009 293000 125 6500 0
## 93 2019 31974 150 75900 0
## 94 2015 298000 190 49999 0
## 95 2009 225000 150 26900 0
## 96 2018 62000 190 169800 0
## 97 2005 181506 256 26000 0
## 98 2010 182930 155 31000 0
## 99 2012 136248 160 55000 0
## 100 2003 276313 110 14500 0
## 101 2006 218114 102 14000 0
## 102 2016 108000 320 144900 0
## 103 2011 199000 110 23900 0
## 104 2008 206000 122 22900 0
## 105 2013 179000 175 45600 0
## 106 2015 78000 130 60900 0
## 107 2011 242000 116 27900 0
## 108 2010 249366 163 53000 0
## 109 2010 144000 107 23900 0
## 110 2007 179500 112 11600 0
## 111 2007 196500 174 34900 0
## 112 2006 219000 136 15900 0
## 113 2005 200000 90 13200 0
## 114 2011 124000 75 15900 0
## 115 2005 154000 109 14900 0
## 116 2010 190000 115 29900 0
## 117 2004 250000 109 35900 0
## 118 2009 199500 105 12900 0
## 119 2016 175000 112 49500 0
## 120 2010 209500 150 35900 0
## 121 2005 245000 367 35900 0
## 122 2004 240000 143 12900 0
## 123 2004 237500 82 6900 0
## 124 2011 84500 101 26500 0
## 125 2004 150000 265 25900 0
## 126 2011 250000 110 20000 0
## 127 2014 155000 86 31900 0
## 128 2017 129000 177 99500 0
## 129 2007 229000 140 12600 0
## 130 2008 220000 150 31500 0
## 131 2007 199000 150 14800 0
## 132 2002 169000 75 8500 0
## 133 2016 123000 163 97000 0
## 134 2008 190000 165 22500 0
## 135 1999 181000 136 15500 0
## 136 2003 143000 106 8700 0
## 137 2012 143000 100 33500 0
## 138 2007 110000 150 10500 0
## 139 2006 165000 218 26900 0
## 140 2007 199000 140 15900 0
## 141 2012 164000 69 16900 0
## 142 1997 228000 193 13500 0
## 143 2014 220000 163 39900 0
## 144 2005 165000 87 10500 0
## 145 2010 171000 105 26300 0
## 146 2015 176000 120 37900 1
## 147 2009 103876 258 59000 0
## 148 2013 268500 130 24900 0
## 149 2012 265000 212 39500 0
## 150 2010 280000 240 58500 0
## 151 2008 81000 258 56000 0
## 152 2007 251344 68 5900 0
## 153 2012 150000 90 25900 0
## 154 2011 208000 190 29900 0
## 155 2018 12962 459 129000 0
## 156 2007 151000 129 22900 0
## 157 2017 97000 155 137000 0
## 158 2016 28800 300 230000 0
## 159 1998 289000 75 1999 0
## 160 2012 129000 117 47500 0
## 161 2021 1576 450 219000 0
## 162 1998 282000 90 3900 0
## 163 2016 105000 142 68900 0
## 164 2022 5 150 193152 0
## 165 2016 60000 132 72000 0
## 166 2022 7024 310 235000 0
## 167 2018 43408 95 43000 0
## 168 2003 520000 218 11900 0
## 169 2022 5 150 139997 0
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## 172 2020 10600 80 76900 0
## 173 2017 112192 150 79900 0
## 174 2013 179508 100 67900 0
## 175 2019 37616 192 128900 0
## 176 2005 242000 140 18500 0
## 177 2012 171150 100 25900 0
## 178 2016 180000 110 46000 0
## 179 2006 245000 61 6000 0
## 180 2017 45256 111 63900 0
## 181 2017 83772 98 99900 0
## 182 2021 9045 98 136900 0
## 183 2007 203900 140 9900 0
## 184 2020 42122 76 89900 0
## 185 2013 180000 140 47500 0
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## 187 2022 5 120 104500 0
## 188 2008 235000 136 17500 0
## 189 2015 143000 372 94900 0
## 190 2018 24500 111 62800 0
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## 195 2017 181168 190 84500 0
## 196 2021 12202 72 69900 0
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## 201 2010 52076 99 35500 0
## 202 2016 91000 149 65000 0
## 203 2022 4 129 100390 0
## 204 2009 238205 245 61800 0
## 205 2009 220000 125 19900 0
## 206 2005 260000 105 12800 0
## 207 2010 252000 114 49900 0
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## 216 2022 1 350 289900 0
## 217 2012 190000 163 29999 0
## 218 2019 123405 125 58900 0
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## 220 2013 137000 115 31000 0
## 221 2014 119000 95 34900 0
## 222 2016 85000 136 79999 0
## 223 2012 194000 140 62900 0
## 224 2018 69511 231 93000 0
## 225 2003 246857 102 9800 0
## 226 2017 78000 120 68900 0
## 227 2013 133640 114 32000 0
## 228 2016 107777 115 49999 0
## 229 2018 89000 120 71000 0
## 230 2007 222852 124 23000 0
## 231 2005 190669 102 24000 0
## 232 2011 246211 140 38700 0
## 233 2012 107000 105 35000 0
## 234 2009 189000 90 21999 0
## 235 2011 215000 170 27999 1
## 236 2014 189000 258 175000 0
## 237 2007 297752 115 14500 0
## 238 2019 48000 175 132000 0
## 239 2014 175000 380 166000 0
## 240 2011 212000 122 28500 0
## 241 2009 206600 102 19200 0
## 242 2005 237997 135 15900 0
## 243 2010 225000 163 37300 0
## 244 2016 133000 320 155581 0
## 245 2015 327000 163 33500 0
## 246 2013 177000 140 64000 0
## 247 2011 200000 120 17900 0
## Marka_pojazdu_Audi Marka_pojazdu_BMW Marka_pojazdu_Cadillac
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## Marka_pojazdu_Chevrolet Marka_pojazdu_Chrysler Marka_pojazdu_Citroën
## 1 0 0 0
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## Model_pojazdu_3008 Model_pojazdu_300C Model_pojazdu_300s Model_pojazdu_301
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## Model_pojazdu_407 Model_pojazdu_5 Model_pojazdu_500 Model_pojazdu_500X
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## Model_pojazdu_508 Model_pojazdu_6 Model_pojazdu_626
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## Model_pojazdu_718 Boxster Model_pojazdu_718 Cayman Model_pojazdu_80
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## Model_pojazdu_9-3 Model_pojazdu_911 Model_pojazdu_A1 Model_pojazdu_A3
## 1 0 0 0 0
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## 247 0 0 0 0
## Model_pojazdu_A4 Model_pojazdu_A5 Model_pojazdu_A6 Model_pojazdu_A6 Allroad
## 1 0 0 0 0
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## Model_pojazdu_A7 Model_pojazdu_A8 Model_pojazdu_Accord Model_pojazdu_Adam
## 1 0 0 0 0
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## Model_pojazdu_Altea Model_pojazdu_Altea XL Model_pojazdu_AMG GT
## 1 0 0 0
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## Model_pojazdu_Antara Model_pojazdu_Arteon Model_pojazdu_Astra
## 1 0 0 0
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## Model_pojazdu_ASX Model_pojazdu_Ateca Model_pojazdu_Auris
## 1 0 0 0
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## Model_pojazdu_Avensis Model_pojazdu_Aveo Model_pojazdu_Aygo
## 1 0 0 0
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## Model_pojazdu_Baleno Model_pojazdu_Berlingo Model_pojazdu_BLS
## 1 0 0 0
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## Model_pojazdu_Bravo Model_pojazdu_C-HR Model_pojazdu_C-MAX Model_pojazdu_C1
## 1 0 0 0 0
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## Model_pojazdu_C3 Model_pojazdu_C3 Picasso Model_pojazdu_C30
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## Model_pojazdu_C4 Model_pojazdu_C4 Cactus Model_pojazdu_C4 Grand Picasso
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## Model_pojazdu_C4 Picasso Model_pojazdu_C5 Model_pojazdu_C5 Aircross
## 1 0 0 0
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## Model_pojazdu_C6 Model_pojazdu_C8 Model_pojazdu_Camry Model_pojazdu_Captiva
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## Model_pojazdu_Captur Model_pojazdu_Carens Model_pojazdu_Cayenne
## 1 0 0 0
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## Model_pojazdu_Ceed Model_pojazdu_Challenger Model_pojazdu_Charger
## 1 0 0 0
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## Model_pojazdu_Cherokee Model_pojazdu_City Model_pojazdu_Civic
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## Model_pojazdu_CL Model_pojazdu_CLA Model_pojazdu_Clio Model_pojazdu_CLS
## 1 0 0 0 0
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## Model_pojazdu_Clubman Model_pojazdu_Colt Model_pojazdu_Combo
## 1 0 0 0
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## Model_pojazdu_Compass Model_pojazdu_Cooper Model_pojazdu_Cooper S
## 1 0 0 0
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## Model_pojazdu_Corolla Model_pojazdu_Corsa Model_pojazdu_Countryman
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## Model_pojazdu_CR-V Model_pojazdu_Crafter Model_pojazdu_Crossland X
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## Model_pojazdu_Cruze Model_pojazdu_CTS Model_pojazdu_CX Model_pojazdu_CX-3
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## Model_pojazdu_CX-5 Model_pojazdu_CX-9 Model_pojazdu_Delta
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## Model_pojazdu_Discovery Model_pojazdu_Doblo Model_pojazdu_DS4
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## Model_pojazdu_DS5 Model_pojazdu_Duster Model_pojazdu_EcoSport
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## Model_pojazdu_EDGE Model_pojazdu_Elantra Model_pojazdu_EQC
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## Model_pojazdu_EQS Model_pojazdu_Escape Model_pojazdu_Espace
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## Model_pojazdu_Exeo Model_pojazdu_Expert Model_pojazdu_F150
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## Model_pojazdu_Fabia Model_pojazdu_Fiesta Model_pojazdu_Focus
## 1 0 0 0
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## Model_pojazdu_Focus C-Max Model_pojazdu_Forester Model_pojazdu_Forfour
## 1 0 0 0
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## Model_pojazdu_Freemont Model_pojazdu_Fusion Model_pojazdu_FX
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## Model_pojazdu_Galaxy Model_pojazdu_Garbus Model_pojazdu_Giulia
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## Model_pojazdu_Grand Scenic Model_pojazdu_Grand Vitara
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## Model_pojazdu_Grand Voyager Model_pojazdu_Grande Punto
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## Model_pojazdu_Grandis Model_pojazdu_Grandland Model_pojazdu_Grandland X
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## Model_pojazdu_HR-V Model_pojazdu_i10 Model_pojazdu_i20 Model_pojazdu_I30
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## Model_pojazdu_i30 N Model_pojazdu_i40 Model_pojazdu_Ibiza
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## Model_pojazdu_Impala Model_pojazdu_Inny Model_pojazdu_Insignia
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## Model_pojazdu_IS Model_pojazdu_ix35 Model_pojazdu_Jazz Model_pojazdu_Jetta
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## Model_pojazdu_Journey Model_pojazdu_Juke Model_pojazdu_Kadjar
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## Model_pojazdu_Kamiq Model_pojazdu_Kangoo Model_pojazdu_Klasa B
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## Model_pojazdu_Klasa C Model_pojazdu_Klasa E Model_pojazdu_Klasa S
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## Model_pojazdu_Kodiaq Model_pojazdu_Koleos Model_pojazdu_Kona
## 1 0 0 0
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## Model_pojazdu_Korando Model_pojazdu_Kuga Model_pojazdu_Laguna
## 1 0 0 0
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## Model_pojazdu_Lancer Model_pojazdu_Land Cruiser Model_pojazdu_Legacy
## 1 0 0 0
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## Model_pojazdu_Leon Model_pojazdu_Liana Model_pojazdu_Liberty
## 1 0 0 0
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## Model_pojazdu_Linea Model_pojazdu_Lodgy Model_pojazdu_Logan
## 1 0 0 0
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## Model_pojazdu_LX Model_pojazdu_Macan Model_pojazdu_Magentis
## 1 0 0 0
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## Model_pojazdu_Matrix Model_pojazdu_Megane Model_pojazdu_Meriva
## 1 0 0 0
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## Model_pojazdu_Micra Model_pojazdu_Mito Model_pojazdu_Modus
## 1 0 0 0
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## Model_pojazdu_Mokka Model_pojazdu_Mondeo Model_pojazdu_Murcielago
## 1 0 0 0
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## Model_pojazdu_Mustang Model_pojazdu_Navara Model_pojazdu_New Beetle
## 1 0 0 0
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## Model_pojazdu_Niro Model_pojazdu_Note Model_pojazdu_NX
## 1 0 0 0
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## Model_pojazdu_Octavia Model_pojazdu_ONE Model_pojazdu_Outback
## 1 0 0 0
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## Model_pojazdu_Outlander Model_pojazdu_Pacifica Model_pojazdu_Panda
## 1 0 0 0
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## Model_pojazdu_Partner Model_pojazdu_Passat Model_pojazdu_Patrol
## 1 0 0 0
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## Model_pojazdu_Picanto Model_pojazdu_Polo Model_pojazdu_Prius
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## Model_pojazdu_Prius+ Model_pojazdu_Pro_cee'd Model_pojazdu_ProAce
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## Model_pojazdu_Punto Model_pojazdu_Punto 2012 Model_pojazdu_Q3
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## Model_pojazdu_Q5 Model_pojazdu_Q7 Model_pojazdu_Q8 Model_pojazdu_Qashqai
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## Model_pojazdu_Qashqai+2 Model_pojazdu_Qubo Model_pojazdu_QX70
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## Model_pojazdu_Range Rover Sport Model_pojazdu_RAPID Model_pojazdu_RAV4
## 1 0 0 0
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## Model_pojazdu_Rifter Model_pojazdu_Rio Model_pojazdu_S-Max Model_pojazdu_S3
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## Model_pojazdu_S40 Model_pojazdu_S60 Model_pojazdu_S80 Model_pojazdu_Sandero
## 1 0 0 0 0
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## Model_pojazdu_Santa Fe Model_pojazdu_Scala Model_pojazdu_Scenic
## 1 0 0 0
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## Model_pojazdu_Scirocco Model_pojazdu_Scudo Model_pojazdu_Sedici
## 1 0 0 0
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## 247 0 0 0
## Model_pojazdu_Seria 1 Model_pojazdu_Seria 2 Model_pojazdu_Seria 3
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
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## 192 0 0 1
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## 244 0 0 0
## 245 0 0 0
## 246 0 0 0
## 247 0 0 0
## Model_pojazdu_Seria 4 Model_pojazdu_Seria 5 Model_pojazdu_Seria 6
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
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## 244 0 0 0
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## 247 0 0 0
## Model_pojazdu_Seria 7 Model_pojazdu_Seria 8 Model_pojazdu_Seria 900
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
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## Model_pojazdu_Seria B Model_pojazdu_Sharan Model_pojazdu_Sierra
## 1 0 0 0
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## Model_pojazdu_Signum Model_pojazdu_Sirion Model_pojazdu_SL
## 1 0 0 0
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## Model_pojazdu_SLK Model_pojazdu_Sonata Model_pojazdu_Sorento
## 1 0 0 0
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## Model_pojazdu_Soul Model_pojazdu_Space Star Model_pojazdu_Sportage
## 1 0 0 0
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## Model_pojazdu_Stelvio Model_pojazdu_Stonic Model_pojazdu_Superb
## 1 0 0 0
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## Model_pojazdu_Swift Model_pojazdu_SX4 Model_pojazdu_SX4 S-Cross
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## Model_pojazdu_T-Roc Model_pojazdu_Talisman Model_pojazdu_Tercel
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## Model_pojazdu_Terios Model_pojazdu_Testarossa Model_pojazdu_Tigra
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## Model_pojazdu_Tiguan Model_pojazdu_Tipo Model_pojazdu_Toledo
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## Model_pojazdu_Tonale Model_pojazdu_Touareg Model_pojazdu_Touran
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## Model_pojazdu_Tourneo Courier Model_pojazdu_Town & Country
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## Model_pojazdu_Trafic Model_pojazdu_Transit Model_pojazdu_Transporter
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## Model_pojazdu_Trax Model_pojazdu_Tribeca Model_pojazdu_TT
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## Model_pojazdu_Tucson Model_pojazdu_V40 Model_pojazdu_V50 Model_pojazdu_V60
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## 194 0 0 0 0
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## 220 0 0 0 0
## 221 0 0 0 0
## 222 1 0 0 0
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## 225 0 0 0 0
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## 247 0 0 0 0
## Model_pojazdu_V70 Model_pojazdu_V90 Cross Country Model_pojazdu_Vectra
## 1 0 0 0
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## Model_pojazdu_Venga Model_pojazdu_Verso Model_pojazdu_Vitara
## 1 0 0 0
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## 3 0 0 0
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## Model_pojazdu_Vito Model_pojazdu_Vivaro Model_pojazdu_W124 (1984-1993)
## 1 0 0 0
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## Model_pojazdu_Wrangler Model_pojazdu_X-Trail Model_pojazdu_X1
## 1 0 0 0
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## 247 0 0 0
## Model_pojazdu_X2 Model_pojazdu_X3 Model_pojazdu_X4 Model_pojazdu_X5
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
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## Model_pojazdu_X5 M Model_pojazdu_X6 Model_pojazdu_XC 40 Model_pojazdu_XC 60
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## Model_pojazdu_XC 70 Model_pojazdu_XC 90 Model_pojazdu_XCeed
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## Model_pojazdu_XE Model_pojazdu_XF Model_pojazdu_XL7
## 1 0 0 0
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## Model_pojazdu_Xsara Picasso Model_pojazdu_Yaris Model_pojazdu_Zafira
## 1 0 0 0
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## 121 0 0 0
## 122 0 0 0
## 123 0 0 0
## 124 0 0 0
## 125 0 0 0
## 126 0 0 0
## 127 0 0 0
## 128 0 0 0
## 129 0 0 0
## 130 0 0 0
## 131 0 0 0
## 132 0 0 0
## 133 0 0 0
## 134 0 0 0
## 135 0 0 0
## 136 0 0 0
## 137 0 0 0
## 138 0 0 0
## 139 0 0 0
## 140 0 0 0
## 141 0 0 0
## 142 0 0 0
## 143 0 0 0
## 144 0 0 0
## 145 0 0 0
## 146 0 0 0
## 147 0 0 0
## 148 0 0 0
## 149 0 0 0
## 150 0 0 0
## 151 0 0 0
## 152 0 0 0
## 153 0 0 0
## 154 0 0 0
## 155 0 0 0
## 156 0 0 0
## 157 0 0 0
## 158 0 0 0
## 159 0 0 0
## 160 0 0 0
## 161 0 0 0
## 162 0 0 0
## 163 0 0 0
## 164 0 0 0
## 165 0 0 0
## 166 0 0 0
## 167 0 0 0
## 168 0 0 0
## 169 0 0 0
## 170 0 0 0
## 171 0 0 0
## 172 0 0 0
## 173 0 0 0
## 174 0 0 0
## 175 0 0 0
## 176 0 0 0
## 177 0 0 0
## 178 0 0 0
## 179 0 0 0
## 180 0 1 0
## 181 0 0 0
## 182 0 0 0
## 183 0 0 0
## 184 0 0 0
## 185 0 0 0
## 186 0 0 0
## 187 0 0 0
## 188 0 0 0
## 189 0 0 0
## 190 0 1 0
## 191 0 0 0
## 192 0 0 0
## 193 0 0 0
## 194 0 0 0
## 195 0 0 0
## 196 0 1 0
## 197 0 0 0
## 198 0 0 0
## 199 0 0 0
## 200 0 0 0
## 201 0 0 0
## 202 0 0 0
## 203 0 0 0
## 204 0 0 0
## 205 0 0 0
## 206 0 0 0
## 207 0 0 0
## 208 0 0 0
## 209 0 0 0
## 210 0 0 0
## 211 0 0 0
## 212 0 0 0
## 213 0 0 0
## 214 0 0 0
## 215 0 0 0
## 216 0 0 0
## 217 0 0 0
## 218 0 0 0
## 219 0 0 0
## 220 0 0 0
## 221 0 0 0
## 222 0 0 0
## 223 0 0 0
## 224 0 0 0
## 225 0 0 0
## 226 0 0 0
## 227 0 0 0
## 228 0 0 0
## 229 0 0 0
## 230 0 0 0
## 231 0 0 0
## 232 0 0 0
## 233 0 0 0
## 234 0 0 0
## 235 0 0 0
## 236 0 0 0
## 237 0 0 0
## 238 0 0 0
## 239 0 0 0
## 240 0 0 0
## 241 0 0 0
## 242 0 0 0
## 243 0 0 0
## 244 0 0 0
## 245 0 0 0
## 246 0 0 0
## 247 0 0 0
## Model_pojazdu_Zoe Rodzaj_paliwa_Benzyna Rodzaj_paliwa_Benzyna+LPG
## 1 0 1 0
## 2 0 0 0
## 3 0 0 0
## 4 0 1 0
## 5 0 0 0
## 6 0 0 0
## 7 0 1 0
## 8 0 0 0
## 9 0 0 0
## 10 0 1 0
## 11 0 0 0
## 12 0 1 0
## 13 0 1 0
## 14 0 0 0
## 15 0 0 1
## 16 0 1 0
## 17 0 0 0
## 18 0 1 0
## 19 0 1 0
## 20 0 0 0
## 21 0 1 0
## 22 0 1 0
## 23 0 0 0
## 24 0 1 0
## 25 0 1 0
## 26 0 0 0
## 27 0 1 0
## 28 0 1 0
## 29 0 0 1
## 30 0 1 0
## 31 0 1 0
## 32 0 0 1
## 33 0 0 0
## 34 0 1 0
## 35 0 1 0
## 36 0 0 0
## 37 0 1 0
## 38 0 1 0
## 39 0 1 0
## 40 0 0 1
## 41 0 0 0
## 42 0 1 0
## 43 0 0 0
## 44 0 0 1
## 45 0 1 0
## 46 0 1 0
## 47 0 0 0
## 48 0 0 0
## 49 0 0 0
## 50 0 0 0
## 51 0 0 0
## 52 0 0 0
## 53 0 0 0
## 54 0 0 1
## 55 0 0 0
## 56 0 1 0
## 57 0 0 0
## 58 0 0 1
## 59 0 0 1
## 60 0 1 0
## 61 0 1 0
## 62 0 1 0
## 63 0 1 0
## 64 0 0 0
## 65 0 0 0
## 66 0 1 0
## 67 0 1 0
## 68 0 1 0
## 69 0 0 0
## 70 0 1 0
## 71 0 0 0
## 72 0 0 0
## 73 0 0 1
## 74 0 0 0
## 75 0 0 0
## 76 0 1 0
## 77 0 1 0
## 78 0 1 0
## 79 0 0 0
## 80 0 1 0
## 81 0 0 0
## 82 0 1 0
## 83 0 0 0
## 84 0 1 0
## 85 0 0 0
## 86 0 0 1
## 87 0 0 0
## 88 0 0 0
## 89 0 0 0
## 90 0 0 0
## 91 0 0 1
## 92 0 1 0
## 93 0 1 0
## 94 0 0 0
## 95 0 1 0
## 96 0 0 0
## 97 0 1 0
## 98 0 1 0
## 99 0 1 0
## 100 0 0 0
## 101 0 1 0
## 102 0 1 0
## 103 0 0 0
## 104 0 1 0
## 105 0 0 0
## 106 0 0 0
## 107 0 0 0
## 108 0 0 0
## 109 0 0 1
## 110 0 1 0
## 111 0 0 0
## 112 0 0 0
## 113 0 1 0
## 114 0 0 0
## 115 0 1 0
## 116 0 0 1
## 117 0 0 0
## 118 0 1 0
## 119 0 0 0
## 120 0 0 0
## 121 0 1 0
## 122 0 1 0
## 123 0 0 0
## 124 0 1 0
## 125 0 1 0
## 126 0 0 0
## 127 0 1 0
## 128 0 0 0
## 129 0 0 0
## 130 0 0 0
## 131 0 0 0
## 132 0 1 0
## 133 0 0 0
## 134 0 0 1
## 135 0 1 0
## 136 0 1 0
## 137 0 1 0
## 138 0 0 0
## 139 0 0 1
## 140 0 0 0
## 141 0 1 0
## 142 0 1 0
## 143 0 0 0
## 144 0 1 0
## 145 0 1 0
## 146 0 0 1
## 147 0 1 0
## 148 0 0 0
## 149 0 1 0
## 150 0 0 0
## 151 0 1 0
## 152 0 1 0
## 153 0 0 0
## 154 0 0 0
## 155 0 1 0
## 156 0 1 0
## 157 0 0 0
## 158 0 1 0
## 159 0 1 0
## 160 0 1 0
## 161 0 1 0
## 162 0 0 0
## 163 0 0 1
## 164 0 0 0
## 165 0 1 0
## 166 0 1 0
## 167 0 1 0
## 168 0 0 0
## 169 0 1 0
## 170 0 0 0
## 171 0 0 0
## 172 0 1 0
## 173 0 0 0
## 174 0 0 0
## 175 0 1 0
## 176 0 0 0
## 177 0 1 0
## 178 0 0 0
## 179 0 1 0
## 180 0 1 0
## 181 0 0 0
## 182 0 0 0
## 183 0 0 0
## 184 0 0 0
## 185 0 1 0
## 186 0 0 1
## 187 0 1 0
## 188 0 0 0
## 189 0 1 0
## 190 0 1 0
## 191 0 0 0
## 192 0 0 0
## 193 0 0 0
## 194 0 1 0
## 195 0 0 0
## 196 0 1 0
## 197 0 0 0
## 198 0 0 0
## 199 0 0 1
## 200 0 1 0
## 201 0 1 0
## 202 0 1 0
## 203 0 0 0
## 204 0 0 0
## 205 0 1 0
## 206 0 0 0
## 207 0 0 0
## 208 0 0 0
## 209 0 1 0
## 210 0 0 0
## 211 0 1 0
## 212 0 0 0
## 213 0 1 0
## 214 0 1 0
## 215 0 0 1
## 216 0 0 0
## 217 0 0 0
## 218 0 1 0
## 219 0 0 0
## 220 0 1 0
## 221 0 0 0
## 222 0 0 0
## 223 0 0 0
## 224 0 1 0
## 225 0 1 0
## 226 0 0 0
## 227 0 0 0
## 228 0 0 0
## 229 0 0 0
## 230 0 1 0
## 231 0 0 1
## 232 0 0 0
## 233 0 0 1
## 234 0 0 0
## 235 0 1 0
## 236 0 0 0
## 237 0 0 0
## 238 0 1 0
## 239 0 0 0
## 240 0 1 0
## 241 0 1 0
## 242 0 1 0
## 243 0 0 0
## 244 0 0 0
## 245 0 0 0
## 246 0 0 0
## 247 0 1 0
## Rodzaj_paliwa_Diesel Rodzaj_paliwa_Elektryczny Rodzaj_paliwa_Hybryda
## 1 0 0 0
## 2 1 0 0
## 3 1 0 0
## 4 0 0 0
## 5 0 0 1
## 6 1 0 0
## 7 0 0 0
## 8 1 0 0
## 9 1 0 0
## 10 0 0 0
## 11 1 0 0
## 12 0 0 0
## 13 0 0 0
## 14 1 0 0
## 15 0 0 0
## 16 0 0 0
## 17 1 0 0
## 18 0 0 0
## 19 0 0 0
## 20 1 0 0
## 21 0 0 0
## 22 0 0 0
## 23 0 0 1
## 24 0 0 0
## 25 0 0 0
## 26 1 0 0
## 27 0 0 0
## 28 0 0 0
## 29 0 0 0
## 30 0 0 0
## 31 0 0 0
## 32 0 0 0
## 33 1 0 0
## 34 0 0 0
## 35 0 0 0
## 36 0 0 1
## 37 0 0 0
## 38 0 0 0
## 39 0 0 0
## 40 0 0 0
## 41 1 0 0
## 42 0 0 0
## 43 1 0 0
## 44 0 0 0
## 45 0 0 0
## 46 0 0 0
## 47 1 0 0
## 48 0 0 1
## 49 1 0 0
## 50 1 0 0
## 51 1 0 0
## 52 1 0 0
## 53 1 0 0
## 54 0 0 0
## 55 1 0 0
## 56 0 0 0
## 57 1 0 0
## 58 0 0 0
## 59 0 0 0
## 60 0 0 0
## 61 0 0 0
## 62 0 0 0
## 63 0 0 0
## 64 1 0 0
## 65 1 0 0
## 66 0 0 0
## 67 0 0 0
## 68 0 0 0
## 69 1 0 0
## 70 0 0 0
## 71 1 0 0
## 72 1 0 0
## 73 0 0 0
## 74 0 0 1
## 75 1 0 0
## 76 0 0 0
## 77 0 0 0
## 78 0 0 0
## 79 1 0 0
## 80 0 0 0
## 81 1 0 0
## 82 0 0 0
## 83 1 0 0
## 84 0 0 0
## 85 1 0 0
## 86 0 0 0
## 87 1 0 0
## 88 1 0 0
## 89 1 0 0
## 90 1 0 0
## 91 0 0 0
## 92 0 0 0
## 93 0 0 0
## 94 1 0 0
## 95 0 0 0
## 96 1 0 0
## 97 0 0 0
## 98 0 0 0
## 99 0 0 0
## 100 1 0 0
## 101 0 0 0
## 102 0 0 0
## 103 1 0 0
## 104 0 0 0
## 105 1 0 0
## 106 1 0 0
## 107 1 0 0
## 108 1 0 0
## 109 0 0 0
## 110 0 0 0
## 111 1 0 0
## 112 1 0 0
## 113 0 0 0
## 114 1 0 0
## 115 0 0 0
## 116 0 0 0
## 117 1 0 0
## 118 0 0 0
## 119 1 0 0
## 120 1 0 0
## 121 0 0 0
## 122 0 0 0
## 123 1 0 0
## 124 0 0 0
## 125 0 0 0
## 126 1 0 0
## 127 0 0 0
## 128 1 0 0
## 129 1 0 0
## 130 1 0 0
## 131 1 0 0
## 132 0 0 0
## 133 1 0 0
## 134 0 0 0
## 135 0 0 0
## 136 0 0 0
## 137 0 0 0
## 138 1 0 0
## 139 0 0 0
## 140 1 0 0
## 141 0 0 0
## 142 0 0 0
## 143 1 0 0
## 144 0 0 0
## 145 0 0 0
## 146 0 0 0
## 147 0 0 0
## 148 1 0 0
## 149 0 0 0
## 150 1 0 0
## 151 0 0 0
## 152 0 0 0
## 153 1 0 0
## 154 1 0 0
## 155 0 0 0
## 156 0 0 0
## 157 0 0 1
## 158 0 0 0
## 159 0 0 0
## 160 0 0 0
## 161 0 0 0
## 162 1 0 0
## 163 0 0 0
## 164 0 0 1
## 165 0 0 0
## 166 0 0 0
## 167 0 0 0
## 168 1 0 0
## 169 0 0 0
## 170 1 0 0
## 171 1 0 0
## 172 0 0 0
## 173 1 0 0
## 174 0 0 1
## 175 0 0 0
## 176 1 0 0
## 177 0 0 0
## 178 1 0 0
## 179 0 0 0
## 180 0 0 0
## 181 0 0 1
## 182 0 0 1
## 183 1 0 0
## 184 1 0 0
## 185 0 0 0
## 186 0 0 0
## 187 0 0 0
## 188 1 0 0
## 189 0 0 0
## 190 0 0 0
## 191 1 0 0
## 192 1 0 0
## 193 1 0 0
## 194 0 0 0
## 195 1 0 0
## 196 0 0 0
## 197 1 0 0
## 198 1 0 0
## 199 0 0 0
## 200 0 0 0
## 201 0 0 0
## 202 0 0 0
## 203 0 0 1
## 204 1 0 0
## 205 0 0 0
## 206 1 0 0
## 207 1 0 0
## 208 1 0 0
## 209 0 0 0
## 210 1 0 0
## 211 0 0 0
## 212 1 0 0
## 213 0 0 0
## 214 0 0 0
## 215 0 0 0
## 216 0 0 1
## 217 1 0 0
## 218 0 0 0
## 219 1 0 0
## 220 0 0 0
## 221 1 0 0
## 222 1 0 0
## 223 1 0 0
## 224 0 0 0
## 225 0 0 0
## 226 1 0 0
## 227 1 0 0
## 228 1 0 0
## 229 1 0 0
## 230 0 0 0
## 231 0 0 0
## 232 1 0 0
## 233 0 0 0
## 234 1 0 0
## 235 0 0 0
## 236 1 0 0
## 237 1 0 0
## 238 0 0 0
## 239 1 0 0
## 240 0 0 0
## 241 0 0 0
## 242 0 0 0
## 243 1 0 0
## 244 1 0 0
## 245 1 0 0
## 246 1 0 0
## 247 0 0 0
## Typ_nadwozia_Auta małe Typ_nadwozia_Auta miejskie Typ_nadwozia_Coupe
## 1 0 1 0
## 2 0 0 0
## 3 0 0 0
## 4 0 1 0
## 5 0 0 0
## 6 0 0 0
## 7 0 0 0
## 8 0 0 0
## 9 0 0 0
## 10 0 0 0
## 11 0 0 0
## 12 0 0 0
## 13 0 1 0
## 14 0 0 0
## 15 0 0 0
## 16 0 0 0
## 17 0 0 0
## 18 0 0 0
## 19 0 0 0
## 20 0 0 0
## 21 0 0 0
## 22 0 1 0
## 23 1 0 0
## 24 0 1 0
## 25 0 0 0
## 26 0 0 0
## 27 0 0 0
## 28 0 0 0
## 29 0 0 0
## 30 0 1 0
## 31 0 1 0
## 32 0 0 0
## 33 0 0 0
## 34 0 0 0
## 35 0 0 0
## 36 0 0 0
## 37 0 0 0
## 38 0 0 0
## 39 0 0 0
## 40 0 0 0
## 41 0 0 0
## 42 0 0 0
## 43 0 0 0
## 44 0 1 0
## 45 0 0 0
## 46 0 0 0
## 47 0 0 0
## 48 0 0 0
## 49 0 0 0
## 50 0 0 0
## 51 0 1 0
## 52 0 0 0
## 53 0 0 0
## 54 0 0 0
## 55 0 0 0
## 56 0 0 0
## 57 0 0 0
## 58 0 0 0
## 59 0 0 0
## 60 0 0 0
## 61 0 0 0
## 62 0 0 0
## 63 0 0 0
## 64 0 0 0
## 65 0 0 0
## 66 0 0 0
## 67 0 1 0
## 68 0 0 0
## 69 0 0 0
## 70 0 0 0
## 71 0 0 0
## 72 0 0 0
## 73 0 0 0
## 74 0 0 0
## 75 0 0 0
## 76 0 0 0
## 77 0 1 0
## 78 0 0 1
## 79 0 0 0
## 80 0 0 0
## 81 0 0 0
## 82 0 0 0
## 83 0 0 0
## 84 0 0 0
## 85 0 0 0
## 86 0 0 0
## 87 0 0 0
## 88 0 0 0
## 89 0 0 0
## 90 0 0 0
## 91 0 0 0
## 92 1 0 0
## 93 0 0 0
## 94 0 0 0
## 95 0 0 0
## 96 0 0 0
## 97 0 0 0
## 98 0 0 0
## 99 0 0 0
## 100 0 0 0
## 101 0 0 0
## 102 0 0 0
## 103 0 0 0
## 104 0 0 0
## 105 0 0 0
## 106 0 0 0
## 107 0 0 0
## 108 0 0 0
## 109 0 0 0
## 110 0 0 0
## 111 0 0 0
## 112 0 0 0
## 113 0 1 0
## 114 0 0 0
## 115 0 1 0
## 116 0 0 0
## 117 0 0 0
## 118 0 0 0
## 119 0 0 0
## 120 0 0 0
## 121 0 0 0
## 122 0 0 0
## 123 0 0 0
## 124 0 1 0
## 125 0 0 1
## 126 0 0 0
## 127 0 0 0
## 128 0 0 0
## 129 0 0 0
## 130 0 0 0
## 131 0 0 0
## 132 0 1 0
## 133 0 0 0
## 134 0 0 0
## 135 0 0 0
## 136 0 0 0
## 137 0 0 0
## 138 0 0 0
## 139 0 0 0
## 140 0 0 0
## 141 1 0 0
## 142 0 0 0
## 143 0 0 0
## 144 0 1 0
## 145 0 0 0
## 146 0 0 0
## 147 0 0 0
## 148 0 0 0
## 149 0 0 0
## 150 0 0 0
## 151 0 0 0
## 152 1 0 0
## 153 0 0 0
## 154 0 0 1
## 155 0 0 1
## 156 0 0 0
## 157 0 0 0
## 158 0 0 0
## 159 0 0 0
## 160 0 0 0
## 161 0 0 1
## 162 0 0 0
## 163 0 0 0
## 164 0 0 0
## 165 0 0 0
## 166 0 0 0
## 167 0 0 0
## 168 0 0 0
## 169 0 1 0
## 170 0 0 0
## 171 0 1 0
## 172 0 1 0
## 173 0 0 0
## 174 0 0 0
## 175 0 0 0
## 176 0 0 0
## 177 0 0 0
## 178 1 0 0
## 179 1 0 0
## 180 0 0 0
## 181 0 0 0
## 182 0 0 0
## 183 0 0 0
## 184 0 0 0
## 185 0 0 0
## 186 0 0 0
## 187 0 0 0
## 188 0 0 0
## 189 0 0 1
## 190 0 1 0
## 191 0 0 0
## 192 0 0 0
## 193 0 0 0
## 194 0 0 0
## 195 0 0 0
## 196 0 1 0
## 197 0 0 0
## 198 0 0 0
## 199 0 0 0
## 200 0 0 0
## 201 0 0 0
## 202 0 0 0
## 203 0 0 0
## 204 0 0 0
## 205 0 0 0
## 206 1 0 0
## 207 0 0 0
## 208 0 0 0
## 209 0 0 0
## 210 0 0 0
## 211 0 1 0
## 212 0 0 0
## 213 0 1 0
## 214 0 0 0
## 215 0 0 0
## 216 0 0 0
## 217 0 0 0
## 218 0 0 0
## 219 0 0 0
## 220 0 0 0
## 221 0 0 0
## 222 0 0 0
## 223 0 0 0
## 224 0 0 0
## 225 0 0 1
## 226 0 0 0
## 227 0 0 0
## 228 0 0 0
## 229 0 0 0
## 230 0 0 0
## 231 0 0 0
## 232 0 0 0
## 233 0 0 0
## 234 0 0 0
## 235 1 0 0
## 236 0 0 0
## 237 0 0 0
## 238 0 0 0
## 239 0 0 0
## 240 0 0 0
## 241 0 0 0
## 242 0 0 0
## 243 0 0 0
## 244 0 0 0
## 245 0 0 0
## 246 0 0 0
## 247 0 0 0
## Typ_nadwozia_Kabriolet Typ_nadwozia_Kombi Typ_nadwozia_Kompakt
## 1 0 0 0
## 2 0 1 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 1
## 6 0 0 0
## 7 0 0 0
## 8 0 0 0
## 9 0 1 0
## 10 0 0 0
## 11 0 1 0
## 12 0 0 1
## 13 0 0 0
## 14 0 0 0
## 15 0 0 0
## 16 0 0 1
## 17 0 1 0
## 18 0 0 0
## 19 0 0 1
## 20 0 0 0
## 21 0 0 0
## 22 0 0 0
## 23 0 0 0
## 24 0 0 0
## 25 0 1 0
## 26 0 0 0
## 27 0 1 0
## 28 0 0 0
## 29 0 0 0
## 30 0 0 0
## 31 0 0 0
## 32 0 0 1
## 33 0 1 0
## 34 0 0 0
## 35 0 0 0
## 36 0 0 0
## 37 0 0 1
## 38 0 1 0
## 39 0 0 0
## 40 0 0 0
## 41 0 1 0
## 42 0 0 0
## 43 0 1 0
## 44 0 0 0
## 45 0 0 0
## 46 0 0 0
## 47 0 0 0
## 48 0 0 0
## 49 0 1 0
## 50 0 0 0
## 51 0 0 0
## 52 0 1 0
## 53 0 0 1
## 54 0 0 0
## 55 0 0 1
## 56 0 0 0
## 57 0 0 0
## 58 0 0 0
## 59 0 0 0
## 60 0 0 1
## 61 0 0 1
## 62 0 0 0
## 63 0 0 1
## 64 0 0 0
## 65 0 0 0
## 66 0 1 0
## 67 0 0 0
## 68 0 0 0
## 69 0 1 0
## 70 0 0 0
## 71 0 0 0
## 72 0 0 0
## 73 0 0 0
## 74 0 0 0
## 75 0 0 0
## 76 0 0 0
## 77 0 0 0
## 78 0 0 0
## 79 0 0 0
## 80 0 0 0
## 81 0 0 0
## 82 0 0 0
## 83 0 0 1
## 84 0 0 1
## 85 0 0 0
## 86 0 0 0
## 87 0 0 1
## 88 0 0 0
## 89 0 0 1
## 90 0 1 0
## 91 0 1 0
## 92 0 0 0
## 93 0 0 0
## 94 0 1 0
## 95 0 0 0
## 96 0 0 0
## 97 0 1 0
## 98 0 0 1
## 99 0 0 0
## 100 0 1 0
## 101 0 0 1
## 102 0 0 0
## 103 0 0 1
## 104 0 0 0
## 105 0 1 0
## 106 0 0 0
## 107 0 0 0
## 108 0 0 0
## 109 0 0 1
## 110 0 1 0
## 111 0 0 0
## 112 0 0 0
## 113 0 0 0
## 114 0 0 0
## 115 0 0 0
## 116 0 0 0
## 117 0 0 0
## 118 0 0 1
## 119 0 1 0
## 120 0 0 0
## 121 0 0 0
## 122 0 0 0
## 123 0 0 0
## 124 0 0 0
## 125 0 0 0
## 126 0 1 0
## 127 0 0 1
## 128 0 0 0
## 129 0 1 0
## 130 0 0 0
## 131 0 1 0
## 132 0 0 0
## 133 0 0 0
## 134 0 1 0
## 135 1 0 0
## 136 0 0 1
## 137 0 0 1
## 138 0 0 1
## 139 0 0 0
## 140 0 0 0
## 141 0 0 0
## 142 0 1 0
## 143 0 1 0
## 144 0 0 0
## 145 0 0 0
## 146 0 0 1
## 147 0 0 0
## 148 0 0 0
## 149 0 0 0
## 150 0 1 0
## 151 0 0 0
## 152 0 0 0
## 153 0 0 1
## 154 0 0 0
## 155 0 0 0
## 156 0 0 0
## 157 0 0 0
## 158 1 0 0
## 159 0 0 1
## 160 0 0 0
## 161 0 0 0
## 162 0 0 0
## 163 0 1 0
## 164 0 0 1
## 165 0 0 0
## 166 0 0 0
## 167 0 1 0
## 168 0 0 0
## 169 0 0 0
## 170 0 0 1
## 171 0 0 0
## 172 0 0 0
## 173 0 1 0
## 174 0 0 0
## 175 0 1 0
## 176 0 1 0
## 177 0 0 1
## 178 0 0 0
## 179 0 0 0
## 180 0 0 1
## 181 0 0 0
## 182 0 1 0
## 183 0 0 0
## 184 0 0 0
## 185 0 0 0
## 186 0 0 0
## 187 0 0 1
## 188 1 0 0
## 189 0 0 0
## 190 0 0 0
## 191 0 0 0
## 192 0 0 0
## 193 0 0 0
## 194 0 1 0
## 195 0 1 0
## 196 0 0 0
## 197 0 0 0
## 198 0 1 0
## 199 0 1 0
## 200 0 1 0
## 201 0 0 1
## 202 0 0 0
## 203 0 0 0
## 204 0 0 0
## 205 0 1 0
## 206 0 0 0
## 207 0 0 0
## 208 0 0 0
## 209 0 0 1
## 210 0 0 0
## 211 0 0 0
## 212 0 1 0
## 213 0 0 0
## 214 0 0 0
## 215 0 0 0
## 216 0 0 0
## 217 0 0 1
## 218 0 1 0
## 219 0 1 0
## 220 0 1 0
## 221 0 0 1
## 222 0 0 0
## 223 0 0 0
## 224 0 0 0
## 225 0 0 0
## 226 0 0 1
## 227 0 0 0
## 228 0 0 0
## 229 0 0 0
## 230 0 0 1
## 231 0 0 1
## 232 0 0 0
## 233 0 0 1
## 234 0 0 1
## 235 0 0 0
## 236 0 0 0
## 237 0 1 0
## 238 0 1 0
## 239 0 0 0
## 240 0 0 1
## 241 0 0 1
## 242 0 0 0
## 243 0 0 0
## 244 0 1 0
## 245 0 1 0
## 246 0 0 0
## 247 0 1 0
## Typ_nadwozia_Minivan Typ_nadwozia_Sedan Typ_nadwozia_SUV Kolor_Beżowy
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 1 1
## 4 0 0 0 0
## 5 0 0 0 0
## 6 1 0 0 0
## 7 0 0 1 0
## 8 0 1 0 0
## 9 0 0 0 0
## 10 0 0 1 0
## 11 0 0 0 0
## 12 0 0 0 0
## 13 0 0 0 1
## 14 0 1 0 0
## 15 0 1 0 0
## 16 0 0 0 0
## 17 0 0 0 0
## 18 0 0 1 0
## 19 0 0 0 1
## 20 0 0 1 0
## 21 0 0 1 0
## 22 0 0 0 0
## 23 0 0 0 0
## 24 0 0 0 0
## 25 0 0 0 0
## 26 1 0 0 0
## 27 0 0 0 0
## 28 0 0 1 0
## 29 0 1 0 0
## 30 0 0 0 0
## 31 0 0 0 0
## 32 0 0 0 0
## 33 0 0 0 0
## 34 1 0 0 0
## 35 1 0 0 0
## 36 0 0 1 0
## 37 0 0 0 0
## 38 0 0 0 0
## 39 0 0 1 0
## 40 1 0 0 0
## 41 0 0 0 0
## 42 0 1 0 0
## 43 0 0 0 0
## 44 0 0 0 0
## 45 0 1 0 0
## 46 0 0 1 0
## 47 1 0 0 0
## 48 0 0 1 0
## 49 0 0 0 0
## 50 1 0 0 0
## 51 0 0 0 0
## 52 0 0 0 0
## 53 0 0 0 0
## 54 0 1 0 0
## 55 0 0 0 0
## 56 0 1 0 0
## 57 0 0 1 0
## 58 0 0 1 0
## 59 0 1 0 0
## 60 0 0 0 0
## 61 0 0 0 0
## 62 0 0 1 0
## 63 0 0 0 0
## 64 1 0 0 0
## 65 1 0 0 0
## 66 0 0 0 0
## 67 0 0 0 0
## 68 0 1 0 0
## 69 0 0 0 0
## 70 0 1 0 0
## 71 1 0 0 0
## 72 0 0 1 0
## 73 0 1 0 0
## 74 0 0 1 0
## 75 0 1 0 0
## 76 0 1 0 0
## 77 0 0 0 0
## 78 0 0 0 0
## 79 0 0 1 0
## 80 0 1 0 0
## 81 0 1 0 0
## 82 0 1 0 0
## 83 0 0 0 0
## 84 0 0 0 0
## 85 0 0 1 0
## 86 0 1 0 0
## 87 0 0 0 0
## 88 0 0 1 0
## 89 0 0 0 0
## 90 0 0 0 0
## 91 0 0 0 0
## 92 0 0 0 0
## 93 0 0 1 0
## 94 0 0 0 0
## 95 0 1 0 0
## 96 0 0 1 0
## 97 0 0 0 0
## 98 0 0 0 0
## 99 0 0 1 0
## 100 0 0 0 1
## 101 0 0 0 0
## 102 0 0 1 0
## 103 0 0 0 0
## 104 0 1 0 0
## 105 0 0 0 0
## 106 0 0 1 0
## 107 1 0 0 0
## 108 0 0 1 0
## 109 0 0 0 0
## 110 0 0 0 0
## 111 0 0 1 0
## 112 0 1 0 0
## 113 0 0 0 0
## 114 1 0 0 0
## 115 0 0 0 0
## 116 1 0 0 0
## 117 0 0 1 0
## 118 0 0 0 0
## 119 0 0 0 0
## 120 0 0 1 0
## 121 0 1 0 0
## 122 0 1 0 0
## 123 1 0 0 0
## 124 0 0 0 0
## 125 0 0 0 0
## 126 0 0 0 0
## 127 0 0 0 0
## 128 0 0 1 0
## 129 0 0 0 0
## 130 0 0 1 0
## 131 0 0 0 0
## 132 0 0 0 0
## 133 0 0 1 0
## 134 0 0 0 0
## 135 0 0 0 0
## 136 0 0 0 0
## 137 0 0 0 0
## 138 0 0 0 0
## 139 0 0 1 0
## 140 0 1 0 0
## 141 0 0 0 0
## 142 0 0 0 0
## 143 0 0 0 0
## 144 0 0 0 0
## 145 0 0 1 0
## 146 0 0 0 0
## 147 0 1 0 0
## 148 1 0 0 0
## 149 0 0 1 0
## 150 0 0 0 0
## 151 0 1 0 0
## 152 0 0 0 0
## 153 0 0 0 0
## 154 0 0 0 0
## 155 0 0 0 0
## 156 0 1 0 0
## 157 0 0 1 0
## 158 0 0 0 0
## 159 0 0 0 0
## 160 0 0 1 0
## 161 0 0 0 0
## 162 0 1 0 0
## 163 0 0 0 0
## 164 0 0 0 0
## 165 0 0 1 0
## 166 0 1 0 0
## 167 0 0 0 0
## 168 0 1 0 0
## 169 0 0 0 0
## 170 0 0 0 0
## 171 0 0 0 0
## 172 0 0 0 0
## 173 0 0 0 0
## 174 1 0 0 0
## 175 0 0 0 0
## 176 0 0 0 0
## 177 0 0 0 0
## 178 0 0 0 0
## 179 0 0 0 0
## 180 0 0 0 0
## 181 0 0 1 0
## 182 0 0 0 0
## 183 0 1 0 0
## 184 1 0 0 0
## 185 0 0 1 0
## 186 0 0 1 0
## 187 0 0 0 0
## 188 0 0 0 0
## 189 0 0 0 0
## 190 0 0 0 0
## 191 0 1 0 0
## 192 0 1 0 0
## 193 0 0 1 0
## 194 0 0 0 0
## 195 0 0 0 0
## 196 0 0 0 0
## 197 0 0 1 0
## 198 0 0 0 0
## 199 0 0 0 0
## 200 0 0 0 0
## 201 0 0 0 0
## 202 0 1 0 0
## 203 0 0 1 1
## 204 0 1 0 0
## 205 0 0 0 0
## 206 0 0 0 0
## 207 1 0 0 0
## 208 0 0 1 0
## 209 0 0 0 0
## 210 0 1 0 0
## 211 0 0 0 0
## 212 0 0 0 0
## 213 0 0 0 0
## 214 0 1 0 0
## 215 0 0 1 0
## 216 0 0 1 0
## 217 0 0 0 0
## 218 0 0 0 0
## 219 0 0 0 0
## 220 0 0 0 0
## 221 0 0 0 0
## 222 0 0 1 0
## 223 0 0 1 0
## 224 0 0 1 0
## 225 0 0 0 0
## 226 0 0 0 0
## 227 1 0 0 0
## 228 0 0 1 0
## 229 0 1 0 0
## 230 0 0 0 0
## 231 0 0 0 0
## 232 1 0 0 0
## 233 0 0 0 0
## 234 0 0 0 0
## 235 0 0 0 0
## 236 0 0 1 0
## 237 0 0 0 0
## 238 0 0 0 0
## 239 0 0 1 0
## 240 0 0 0 0
## 241 0 0 0 0
## 242 1 0 0 0
## 243 0 0 1 0
## 244 0 0 0 0
## 245 0 0 0 0
## 246 0 0 1 0
## 247 0 0 0 0
## Kolor_Biały Kolor_Błękitny Kolor_Bordowy Kolor_Brązowy Kolor_Czarny
## 1 0 0 0 0 1
## 2 0 0 0 0 0
## 3 0 0 0 0 0
## 4 0 0 0 0 0
## 5 0 0 0 0 0
## 6 0 0 0 0 0
## 7 0 0 0 0 0
## 8 0 0 0 0 0
## 9 0 0 0 0 0
## 10 0 0 0 0 0
## 11 0 0 0 0 0
## 12 1 0 0 0 0
## 13 0 0 0 0 0
## 14 0 0 0 0 0
## 15 0 0 1 0 0
## 16 0 0 0 0 0
## 17 0 0 0 1 0
## 18 1 0 0 0 0
## 19 0 0 0 0 0
## 20 1 0 0 0 0
## 21 0 0 0 0 1
## 22 0 0 0 0 1
## 23 0 0 0 0 1
## 24 0 0 0 0 0
## 25 0 0 0 0 1
## 26 0 0 0 0 0
## 27 0 0 0 1 0
## 28 1 0 0 0 0
## 29 0 0 0 0 0
## 30 0 0 0 0 0
## 31 0 0 0 0 0
## 32 0 0 0 0 0
## 33 0 0 0 0 0
## 34 0 0 0 1 0
## 35 1 0 0 0 0
## 36 0 0 0 0 1
## 37 1 0 0 0 0
## 38 0 0 0 0 0
## 39 0 0 0 0 1
## 40 0 0 0 0 0
## 41 0 0 0 0 0
## 42 0 0 0 0 1
## 43 1 0 0 0 0
## 44 0 0 0 0 0
## 45 0 0 0 0 0
## 46 0 0 0 0 1
## 47 0 0 0 0 0
## 48 1 0 0 0 0
## 49 0 0 0 0 0
## 50 0 0 0 0 0
## 51 0 0 0 0 0
## 52 0 0 0 0 0
## 53 0 0 0 0 1
## 54 0 0 0 0 1
## 55 0 0 0 0 1
## 56 0 0 0 0 1
## 57 0 0 0 0 1
## 58 0 0 0 0 0
## 59 0 0 0 0 0
## 60 0 0 0 0 1
## 61 0 0 0 0 0
## 62 0 0 0 0 0
## 63 0 0 0 0 0
## 64 0 0 0 0 0
## 65 0 0 0 0 0
## 66 0 0 0 0 0
## 67 0 0 0 0 0
## 68 0 0 0 0 1
## 69 0 0 0 0 0
## 70 0 0 0 0 1
## 71 0 0 0 0 0
## 72 0 0 0 0 1
## 73 0 0 0 0 0
## 74 0 0 0 0 1
## 75 0 0 0 0 1
## 76 0 0 0 0 0
## 77 0 0 0 0 0
## 78 0 0 0 0 0
## 79 0 0 0 0 0
## 80 0 0 0 0 0
## 81 1 0 0 0 0
## 82 0 0 0 0 0
## 83 0 0 0 1 0
## 84 0 0 0 0 0
## 85 0 0 0 0 0
## 86 0 0 0 0 0
## 87 0 0 0 0 0
## 88 1 0 0 0 0
## 89 0 0 0 0 1
## 90 0 0 0 0 1
## 91 0 0 0 0 0
## 92 1 0 0 0 0
## 93 1 0 0 0 0
## 94 0 0 0 0 0
## 95 0 0 0 0 0
## 96 0 0 0 0 0
## 97 0 0 0 0 0
## 98 0 0 0 1 0
## 99 0 0 0 0 0
## 100 0 0 0 0 0
## 101 0 0 0 0 0
## 102 0 0 0 0 0
## 103 0 0 0 0 0
## 104 0 0 0 0 0
## 105 0 0 0 0 1
## 106 0 0 0 0 0
## 107 0 0 0 0 1
## 108 0 0 0 0 0
## 109 0 0 0 0 0
## 110 0 0 0 0 1
## 111 0 0 0 0 1
## 112 0 0 1 0 0
## 113 0 0 0 0 0
## 114 0 0 0 0 1
## 115 0 0 0 0 0
## 116 0 0 0 0 0
## 117 0 0 0 0 0
## 118 0 0 0 0 0
## 119 1 0 0 0 0
## 120 1 0 0 0 0
## 121 0 0 0 0 1
## 122 0 0 0 0 0
## 123 0 0 0 0 0
## 124 0 0 0 0 1
## 125 0 0 0 0 1
## 126 0 0 0 0 0
## 127 0 0 1 0 0
## 128 0 0 0 0 0
## 129 0 0 0 0 0
## 130 0 0 0 0 0
## 131 0 0 0 0 0
## 132 0 0 0 0 0
## 133 0 0 0 0 0
## 134 0 0 0 0 1
## 135 0 0 0 0 1
## 136 0 0 0 0 1
## 137 0 0 0 0 0
## 138 0 0 0 0 0
## 139 0 0 0 0 0
## 140 0 0 0 0 0
## 141 0 0 1 0 0
## 142 0 0 0 0 0
## 143 0 0 0 0 1
## 144 0 0 0 0 0
## 145 0 0 0 0 0
## 146 0 0 0 0 1
## 147 0 0 0 0 0
## 148 0 0 0 0 0
## 149 1 0 0 0 0
## 150 0 0 0 0 1
## 151 0 0 0 0 0
## 152 0 0 0 0 0
## 153 0 0 0 0 0
## 154 0 0 0 0 0
## 155 0 0 0 0 0
## 156 0 0 0 0 0
## 157 0 0 0 0 0
## 158 1 0 0 0 0
## 159 0 0 0 0 0
## 160 0 0 0 0 0
## 161 0 0 0 0 0
## 162 0 0 0 0 0
## 163 1 0 0 0 0
## 164 1 0 0 0 0
## 165 0 0 0 0 0
## 166 0 0 0 0 0
## 167 1 0 0 0 0
## 168 0 0 0 0 0
## 169 1 0 0 0 0
## 170 0 0 0 0 0
## 171 0 0 0 0 0
## 172 0 0 0 0 0
## 173 0 0 0 0 0
## 174 0 0 0 0 1
## 175 0 0 0 0 0
## 176 0 0 0 0 1
## 177 0 0 0 0 0
## 178 0 0 0 0 1
## 179 0 0 0 0 0
## 180 0 0 0 0 0
## 181 0 0 0 0 0
## 182 1 0 0 0 0
## 183 0 0 0 0 0
## 184 0 0 0 0 0
## 185 0 0 0 0 0
## 186 0 0 0 0 1
## 187 0 0 0 0 0
## 188 0 0 0 0 1
## 189 0 0 0 0 1
## 190 1 0 0 0 0
## 191 0 0 0 0 1
## 192 0 0 0 0 1
## 193 0 0 0 0 0
## 194 0 0 0 0 0
## 195 0 0 0 0 0
## 196 0 0 0 0 0
## 197 0 0 0 0 1
## 198 0 0 0 0 0
## 199 0 0 0 0 1
## 200 0 0 0 0 1
## 201 0 0 0 0 0
## 202 0 0 0 0 0
## 203 0 0 0 0 0
## 204 0 0 0 0 1
## 205 0 0 0 0 1
## 206 0 0 0 0 1
## 207 0 1 0 0 0
## 208 0 0 0 0 0
## 209 0 0 0 1 0
## 210 0 0 0 0 0
## 211 0 0 0 0 0
## 212 0 0 0 0 1
## 213 0 0 0 0 0
## 214 0 0 0 0 0
## 215 0 0 0 1 0
## 216 0 0 0 1 0
## 217 1 0 0 0 0
## 218 1 0 0 0 0
## 219 0 0 0 0 0
## 220 0 0 0 0 0
## 221 0 0 0 0 0
## 222 1 0 0 0 0
## 223 0 0 0 1 0
## 224 1 0 0 0 0
## 225 0 0 0 0 0
## 226 0 0 0 0 0
## 227 1 0 0 0 0
## 228 1 0 0 0 0
## 229 0 0 0 0 0
## 230 0 0 0 0 0
## 231 0 0 0 0 0
## 232 0 0 0 0 0
## 233 0 0 0 0 0
## 234 0 0 0 0 0
## 235 0 0 0 0 1
## 236 0 0 0 0 0
## 237 0 0 0 0 1
## 238 0 0 1 0 0
## 239 0 0 0 0 0
## 240 0 0 0 0 1
## 241 0 0 0 0 1
## 242 0 0 0 0 0
## 243 0 0 0 0 1
## 244 0 0 0 0 1
## 245 0 0 0 0 1
## 246 0 0 0 1 0
## 247 0 0 0 0 1
## Kolor_Czerwony Kolor_Fioletowy Kolor_Granatowy Kolor_Inny kolor
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 1
## 5 0 0 0 0
## 6 0 0 0 0
## 7 0 0 0 0
## 8 0 0 1 0
## 9 0 0 0 1
## 10 1 0 0 0
## 11 0 0 0 1
## 12 0 0 0 0
## 13 0 0 0 0
## 14 0 0 0 1
## 15 0 0 0 0
## 16 0 0 0 0
## 17 0 0 0 0
## 18 0 0 0 0
## 19 0 0 0 0
## 20 0 0 0 0
## 21 0 0 0 0
## 22 0 0 0 0
## 23 0 0 0 0
## 24 0 0 0 0
## 25 0 0 0 0
## 26 0 0 0 0
## 27 0 0 0 0
## 28 0 0 0 0
## 29 0 0 0 0
## 30 0 0 0 0
## 31 0 0 0 0
## 32 1 0 0 0
## 33 0 0 0 0
## 34 0 0 0 0
## 35 0 0 0 0
## 36 0 0 0 0
## 37 0 0 0 0
## 38 0 0 0 0
## 39 0 0 0 0
## 40 0 0 0 0
## 41 0 0 0 0
## 42 0 0 0 0
## 43 0 0 0 0
## 44 0 0 0 0
## 45 0 0 1 0
## 46 0 0 0 0
## 47 0 0 0 0
## 48 0 0 0 0
## 49 0 0 0 0
## 50 0 0 0 0
## 51 0 0 0 0
## 52 0 0 0 0
## 53 0 0 0 0
## 54 0 0 0 0
## 55 0 0 0 0
## 56 0 0 0 0
## 57 0 0 0 0
## 58 0 0 0 1
## 59 0 0 0 0
## 60 0 0 0 0
## 61 0 0 0 0
## 62 0 0 0 0
## 63 0 0 0 0
## 64 0 0 0 0
## 65 0 0 0 0
## 66 0 0 0 0
## 67 0 0 0 0
## 68 0 0 0 0
## 69 0 0 0 0
## 70 0 0 0 0
## 71 0 0 0 1
## 72 0 0 0 0
## 73 0 0 0 0
## 74 0 0 0 0
## 75 0 0 0 0
## 76 0 0 1 0
## 77 0 0 1 0
## 78 0 0 0 0
## 79 0 0 0 0
## 80 0 0 0 1
## 81 0 0 0 0
## 82 0 0 1 0
## 83 0 0 0 0
## 84 1 0 0 0
## 85 0 0 0 0
## 86 0 0 0 0
## 87 0 0 0 0
## 88 0 0 0 0
## 89 0 0 0 0
## 90 0 0 0 0
## 91 0 0 0 0
## 92 0 0 0 0
## 93 0 0 0 0
## 94 0 0 0 1
## 95 0 0 0 0
## 96 0 0 0 0
## 97 0 0 0 0
## 98 0 0 0 0
## 99 0 0 0 0
## 100 0 0 0 0
## 101 1 0 0 0
## 102 0 0 1 0
## 103 0 0 0 0
## 104 0 0 0 0
## 105 0 0 0 0
## 106 0 0 0 1
## 107 0 0 0 0
## 108 0 0 0 0
## 109 1 0 0 0
## 110 0 0 0 0
## 111 0 0 0 0
## 112 0 0 0 0
## 113 0 0 0 0
## 114 0 0 0 0
## 115 0 0 0 0
## 116 0 0 0 0
## 117 0 0 0 0
## 118 0 0 0 0
## 119 0 0 0 0
## 120 0 0 0 0
## 121 0 0 0 0
## 122 0 0 0 0
## 123 1 0 0 0
## 124 0 0 0 0
## 125 0 0 0 0
## 126 0 0 0 0
## 127 0 0 0 0
## 128 0 0 0 0
## 129 0 0 0 0
## 130 0 0 0 0
## 131 0 0 0 0
## 132 0 0 0 0
## 133 0 0 0 0
## 134 0 0 0 0
## 135 0 0 0 0
## 136 0 0 0 0
## 137 0 0 0 0
## 138 0 0 0 0
## 139 0 0 0 0
## 140 0 0 0 0
## 141 0 0 0 0
## 142 0 0 0 0
## 143 0 0 0 0
## 144 0 0 0 0
## 145 0 0 0 0
## 146 0 0 0 0
## 147 0 0 0 0
## 148 0 0 0 0
## 149 0 0 0 0
## 150 0 0 0 0
## 151 0 0 0 0
## 152 1 0 0 0
## 153 0 0 0 0
## 154 0 0 0 0
## 155 0 0 0 0
## 156 0 0 0 0
## 157 0 0 0 0
## 158 0 0 0 0
## 159 0 0 1 0
## 160 0 0 0 0
## 161 0 0 1 0
## 162 0 0 1 0
## 163 0 0 0 0
## 164 0 0 0 0
## 165 0 0 0 0
## 166 0 0 0 0
## 167 0 0 0 0
## 168 0 0 0 0
## 169 0 0 0 0
## 170 1 0 0 0
## 171 0 0 1 0
## 172 0 0 0 0
## 173 0 0 0 0
## 174 0 0 0 0
## 175 0 0 0 0
## 176 0 0 0 0
## 177 0 0 0 0
## 178 0 0 0 0
## 179 0 0 0 0
## 180 1 0 0 0
## 181 0 0 0 0
## 182 0 0 0 0
## 183 0 0 0 0
## 184 0 0 0 0
## 185 0 0 0 0
## 186 0 0 0 0
## 187 1 0 0 0
## 188 0 0 0 0
## 189 0 0 0 0
## 190 0 0 0 0
## 191 0 0 0 0
## 192 0 0 0 0
## 193 0 0 0 0
## 194 0 0 0 0
## 195 0 0 0 0
## 196 1 0 0 0
## 197 0 0 0 0
## 198 0 0 0 0
## 199 0 0 0 0
## 200 0 0 0 0
## 201 0 0 0 0
## 202 0 0 0 0
## 203 0 0 0 0
## 204 0 0 0 0
## 205 0 0 0 0
## 206 0 0 0 0
## 207 0 0 0 0
## 208 0 0 0 0
## 209 0 0 0 0
## 210 0 0 0 1
## 211 0 0 0 0
## 212 0 0 0 0
## 213 0 0 0 0
## 214 0 0 0 1
## 215 0 0 0 0
## 216 0 0 0 0
## 217 0 0 0 0
## 218 0 0 0 0
## 219 0 0 0 0
## 220 0 0 0 0
## 221 0 0 0 0
## 222 0 0 0 0
## 223 0 0 0 0
## 224 0 0 0 0
## 225 0 0 0 1
## 226 0 0 0 0
## 227 0 0 0 0
## 228 0 0 0 0
## 229 0 0 0 0
## 230 0 0 0 0
## 231 0 0 0 0
## 232 0 0 0 1
## 233 0 0 0 1
## 234 1 0 0 0
## 235 0 0 0 0
## 236 0 0 0 0
## 237 0 0 0 0
## 238 0 0 0 0
## 239 1 0 0 0
## 240 0 0 0 0
## 241 0 0 0 0
## 242 0 0 0 0
## 243 0 0 0 0
## 244 0 0 0 0
## 245 0 0 0 0
## 246 0 0 0 0
## 247 0 0 0 0
## Kolor_Niebieski Kolor_Pomarańczowy Kolor_Srebrny Kolor_Szary Kolor_Zielony
## 1 0 0 0 0 0
## 2 0 0 0 1 0
## 3 0 0 0 0 0
## 4 0 0 0 0 0
## 5 0 0 0 1 0
## 6 0 0 1 0 0
## 7 0 0 1 0 0
## 8 0 0 0 0 0
## 9 0 0 0 0 0
## 10 0 0 0 0 0
## 11 0 0 0 0 0
## 12 0 0 0 0 0
## 13 0 0 0 0 0
## 14 0 0 0 0 0
## 15 0 0 0 0 0
## 16 0 0 0 1 0
## 17 0 0 0 0 0
## 18 0 0 0 0 0
## 19 0 0 0 0 0
## 20 0 0 0 0 0
## 21 0 0 0 0 0
## 22 0 0 0 0 0
## 23 0 0 0 0 0
## 24 1 0 0 0 0
## 25 0 0 0 0 0
## 26 0 0 0 1 0
## 27 0 0 0 0 0
## 28 0 0 0 0 0
## 29 0 0 0 1 0
## 30 0 0 0 1 0
## 31 1 0 0 0 0
## 32 0 0 0 0 0
## 33 0 0 0 1 0
## 34 0 0 0 0 0
## 35 0 0 0 0 0
## 36 0 0 0 0 0
## 37 0 0 0 0 0
## 38 0 0 0 0 1
## 39 0 0 0 0 0
## 40 0 0 0 1 0
## 41 0 0 0 1 0
## 42 0 0 0 0 0
## 43 0 0 0 0 0
## 44 0 0 1 0 0
## 45 0 0 0 0 0
## 46 0 0 0 0 0
## 47 0 0 1 0 0
## 48 0 0 0 0 0
## 49 1 0 0 0 0
## 50 0 0 0 0 0
## 51 0 0 1 0 0
## 52 0 0 0 1 0
## 53 0 0 0 0 0
## 54 0 0 0 0 0
## 55 0 0 0 0 0
## 56 0 0 0 0 0
## 57 0 0 0 0 0
## 58 0 0 0 0 0
## 59 1 0 0 0 0
## 60 0 0 0 0 0
## 61 0 0 1 0 0
## 62 0 0 1 0 0
## 63 0 0 0 0 0
## 64 0 0 1 0 0
## 65 0 0 1 0 0
## 66 1 0 0 0 0
## 67 0 0 1 0 0
## 68 0 0 0 0 0
## 69 0 0 0 1 0
## 70 0 0 0 0 0
## 71 0 0 0 0 0
## 72 0 0 0 0 0
## 73 0 0 0 0 1
## 74 0 0 0 0 0
## 75 0 0 0 0 0
## 76 0 0 0 0 0
## 77 0 0 0 0 0
## 78 1 0 0 0 0
## 79 1 0 0 0 0
## 80 0 0 0 0 0
## 81 0 0 0 0 0
## 82 0 0 0 0 0
## 83 0 0 0 0 0
## 84 0 0 0 0 0
## 85 0 0 1 0 0
## 86 0 0 0 0 1
## 87 0 0 0 1 0
## 88 0 0 0 0 0
## 89 0 0 0 0 0
## 90 0 0 0 0 0
## 91 0 0 1 0 0
## 92 0 0 0 0 0
## 93 0 0 0 0 0
## 94 0 0 0 0 0
## 95 0 0 1 0 0
## 96 0 0 0 1 0
## 97 0 0 1 0 0
## 98 0 0 0 0 0
## 99 1 0 0 0 0
## 100 0 0 0 0 0
## 101 0 0 0 0 0
## 102 0 0 0 0 0
## 103 1 0 0 0 0
## 104 1 0 0 0 0
## 105 0 0 0 0 0
## 106 0 0 0 0 0
## 107 0 0 0 0 0
## 108 0 0 0 1 0
## 109 0 0 0 0 0
## 110 0 0 0 0 0
## 111 0 0 0 0 0
## 112 0 0 0 0 0
## 113 0 0 1 0 0
## 114 0 0 0 0 0
## 115 1 0 0 0 0
## 116 1 0 0 0 0
## 117 0 0 1 0 0
## 118 0 0 1 0 0
## 119 0 0 0 0 0
## 120 0 0 0 0 0
## 121 0 0 0 0 0
## 122 0 0 1 0 0
## 123 0 0 0 0 0
## 124 0 0 0 0 0
## 125 0 0 0 0 0
## 126 0 0 1 0 0
## 127 0 0 0 0 0
## 128 0 0 0 1 0
## 129 0 0 1 0 0
## 130 0 0 1 0 0
## 131 0 0 1 0 0
## 132 1 0 0 0 0
## 133 0 0 0 1 0
## 134 0 0 0 0 0
## 135 0 0 0 0 0
## 136 0 0 0 0 0
## 137 0 0 1 0 0
## 138 0 0 1 0 0
## 139 0 0 1 0 0
## 140 1 0 0 0 0
## 141 0 0 0 0 0
## 142 0 0 1 0 0
## 143 0 0 0 0 0
## 144 1 0 0 0 0
## 145 0 0 0 1 0
## 146 0 0 0 0 0
## 147 0 0 0 1 0
## 148 0 0 0 1 0
## 149 0 0 0 0 0
## 150 0 0 0 0 0
## 151 0 0 0 1 0
## 152 0 0 0 0 0
## 153 0 0 0 1 0
## 154 1 0 0 0 0
## 155 1 0 0 0 0
## 156 0 0 1 0 0
## 157 0 0 0 1 0
## 158 0 0 0 0 0
## 159 0 0 0 0 0
## 160 0 0 0 1 0
## 161 0 0 0 0 0
## 162 0 0 0 0 0
## 163 0 0 0 0 0
## 164 0 0 0 0 0
## 165 0 0 0 0 0
## 166 0 0 0 1 0
## 167 0 0 0 0 0
## 168 0 0 1 0 0
## 169 0 0 0 0 0
## 170 0 0 0 0 0
## 171 0 0 0 0 0
## 172 0 0 0 1 0
## 173 0 0 1 0 0
## 174 0 0 0 0 0
## 175 0 0 0 1 0
## 176 0 0 0 0 0
## 177 0 0 1 0 0
## 178 0 0 0 0 0
## 179 0 1 0 0 0
## 180 0 0 0 0 0
## 181 0 0 0 1 0
## 182 0 0 0 0 0
## 183 0 0 1 0 0
## 184 0 0 0 1 0
## 185 0 0 0 1 0
## 186 0 0 0 0 0
## 187 0 0 0 0 0
## 188 0 0 0 0 0
## 189 0 0 0 0 0
## 190 0 0 0 0 0
## 191 0 0 0 0 0
## 192 0 0 0 0 0
## 193 0 0 0 1 0
## 194 0 0 1 0 0
## 195 0 0 0 1 0
## 196 0 0 0 0 0
## 197 0 0 0 0 0
## 198 0 0 0 1 0
## 199 0 0 0 0 0
## 200 0 0 0 0 0
## 201 1 0 0 0 0
## 202 0 0 1 0 0
## 203 0 0 0 0 0
## 204 0 0 0 0 0
## 205 0 0 0 0 0
## 206 0 0 0 0 0
## 207 0 0 0 0 0
## 208 0 0 0 0 0
## 209 0 0 0 0 0
## 210 0 0 0 0 0
## 211 0 0 0 0 0
## 212 0 0 0 0 0
## 213 0 0 0 1 0
## 214 0 0 0 0 0
## 215 0 0 0 0 0
## 216 0 0 0 0 0
## 217 0 0 0 0 0
## 218 0 0 0 0 0
## 219 1 0 0 0 0
## 220 0 0 0 1 0
## 221 1 0 0 0 0
## 222 0 0 0 0 0
## 223 0 0 0 0 0
## 224 0 0 0 0 0
## 225 0 0 0 0 0
## 226 0 0 1 0 0
## 227 0 0 0 0 0
## 228 0 0 0 0 0
## 229 0 0 0 1 0
## 230 1 0 0 0 0
## 231 0 0 1 0 0
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## [ reached 'max' / getOption("max.print") -- omitted 1001 rows ]autka_dt_MM_no_eng_capacity <- subset(autka_dt_MM,select = -3)
autka_dt_MM_no_eng_capacity## Rok_produkcji Przebieg Moc Cena Marka_Model_Alfa Romeo 159
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## 269 0 0 0
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## 271 0 0 0
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## 274 0 0 0
## 275 0 0 0
## 276 0 0 0
## 277 0 0 0
## Rodzaj_paliwa_Benzyna Rodzaj_paliwa_Benzyna+LPG Rodzaj_paliwa_Diesel
## 1 1 0 0
## 2 0 0 1
## 3 0 0 1
## 4 1 0 0
## 5 0 0 0
## 6 0 0 1
## 7 1 0 0
## 8 0 0 1
## 9 0 0 1
## 10 1 0 0
## 11 0 0 1
## 12 1 0 0
## 13 1 0 0
## 14 0 0 1
## 15 0 1 0
## 16 1 0 0
## 17 0 0 1
## 18 1 0 0
## 19 1 0 0
## 20 0 0 1
## 21 1 0 0
## 22 1 0 0
## 23 0 0 0
## 24 1 0 0
## 25 1 0 0
## 26 0 0 1
## 27 1 0 0
## 28 1 0 0
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## 30 1 0 0
## 31 1 0 0
## 32 0 1 0
## 33 0 0 1
## 34 1 0 0
## 35 1 0 0
## 36 0 0 0
## 37 1 0 0
## 38 1 0 0
## 39 1 0 0
## 40 0 1 0
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## 43 0 0 1
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## 50 0 0 1
## 51 0 0 1
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## 61 1 0 0
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## 67 1 0 0
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## 143 0 0 1
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## 155 1 0 0
## 156 1 0 0
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## 159 1 0 0
## 160 1 0 0
## 161 1 0 0
## 162 0 0 1
## 163 0 1 0
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## 190 1 0 0
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## 195 0 0 1
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## 201 1 0 0
## 202 1 0 0
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## 207 0 0 1
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## 210 0 0 1
## 211 1 0 0
## 212 0 0 1
## 213 1 0 0
## 214 1 0 0
## 215 0 1 0
## 216 0 0 0
## 217 0 0 1
## 218 1 0 0
## 219 0 0 1
## 220 1 0 0
## 221 0 0 1
## 222 0 0 1
## 223 0 0 1
## 224 1 0 0
## 225 1 0 0
## 226 0 0 1
## 227 0 0 1
## 228 0 0 1
## 229 0 0 1
## 230 1 0 0
## 231 0 1 0
## 232 0 0 1
## 233 0 1 0
## 234 0 0 1
## 235 1 0 0
## 236 0 0 1
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## 239 0 0 1
## 240 1 0 0
## 241 1 0 0
## 242 1 0 0
## 243 0 0 1
## 244 0 0 1
## 245 0 0 1
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## 247 1 0 0
## 248 1 0 0
## 249 0 1 0
## 250 1 0 0
## 251 0 0 1
## 252 0 0 1
## 253 0 0 1
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## 256 0 0 1
## 257 1 0 0
## 258 0 0 1
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## 260 1 0 0
## 261 1 0 0
## 262 0 1 0
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## 264 0 0 1
## 265 1 0 0
## 266 0 0 1
## 267 0 1 0
## 268 0 0 1
## 269 1 0 0
## 270 0 0 1
## 271 0 1 0
## 272 0 0 1
## 273 0 0 1
## 274 1 0 0
## 275 1 0 0
## 276 1 0 0
## 277 0 0 1
## Rodzaj_paliwa_Elektryczny Rodzaj_paliwa_Hybryda Typ_nadwozia_Auta małe
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 1 0
## 6 0 0 0
## 7 0 0 0
## 8 0 0 0
## 9 0 0 0
## 10 0 0 0
## 11 0 0 0
## 12 0 0 0
## 13 0 0 0
## 14 0 0 0
## 15 0 0 0
## 16 0 0 0
## 17 0 0 0
## 18 0 0 0
## 19 0 0 0
## 20 0 0 0
## 21 0 0 0
## 22 0 0 0
## 23 0 1 1
## 24 0 0 0
## 25 0 0 0
## 26 0 0 0
## 27 0 0 0
## 28 0 0 0
## 29 0 0 0
## 30 0 0 0
## 31 0 0 0
## 32 0 0 0
## 33 0 0 0
## 34 0 0 0
## 35 0 0 0
## 36 0 1 0
## 37 0 0 0
## 38 0 0 0
## 39 0 0 0
## 40 0 0 0
## 41 0 0 0
## 42 0 0 0
## 43 0 0 0
## 44 0 0 0
## 45 0 0 0
## 46 0 0 0
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## 50 0 0 0
## 51 0 0 0
## 52 0 0 0
## 53 0 0 0
## 54 0 0 0
## 55 0 0 0
## 56 0 0 0
## 57 0 0 0
## 58 0 0 0
## 59 0 0 0
## 60 0 0 0
## 61 0 0 0
## 62 0 0 0
## 63 0 0 0
## 64 0 0 0
## 65 0 0 0
## 66 0 0 0
## 67 0 0 0
## 68 0 0 0
## 69 0 0 0
## 70 0 0 0
## 71 0 0 0
## 72 0 0 0
## 73 0 0 0
## 74 0 1 0
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## 76 0 0 0
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## 88 0 0 0
## 89 0 0 0
## 90 0 0 0
## 91 0 0 0
## 92 0 0 1
## 93 0 0 0
## 94 0 0 0
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## 134 0 0 0
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## 136 0 0 0
## 137 0 0 0
## 138 0 0 0
## 139 0 0 0
## 140 0 0 0
## 141 0 0 1
## 142 0 0 0
## 143 0 0 0
## 144 0 0 0
## 145 0 0 0
## 146 0 0 0
## 147 0 0 0
## 148 0 0 0
## 149 0 0 0
## 150 0 0 0
## 151 0 0 0
## 152 0 0 1
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## 154 0 0 0
## 155 0 0 0
## 156 0 0 0
## 157 0 1 0
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## 159 0 0 0
## 160 0 0 0
## 161 0 0 0
## 162 0 0 0
## 163 0 0 0
## 164 0 1 0
## 165 0 0 0
## 166 0 0 0
## 167 0 0 0
## 168 0 0 0
## 169 0 0 0
## 170 0 0 0
## 171 0 0 0
## 172 0 0 0
## 173 0 0 0
## 174 0 1 0
## 175 0 0 0
## 176 0 0 0
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## 178 0 0 1
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## 180 0 0 0
## 181 0 1 0
## 182 0 1 0
## 183 0 0 0
## 184 0 0 0
## 185 0 0 0
## 186 0 0 0
## 187 0 0 0
## 188 0 0 0
## 189 0 0 0
## 190 0 0 0
## 191 0 0 0
## 192 0 0 0
## 193 0 0 0
## 194 0 0 0
## 195 0 0 0
## 196 0 0 0
## 197 0 0 0
## 198 0 0 0
## 199 0 0 0
## 200 0 0 0
## 201 0 0 0
## 202 0 0 0
## 203 0 1 0
## 204 0 0 0
## 205 0 0 0
## 206 0 0 1
## 207 0 0 0
## 208 0 0 0
## 209 0 0 0
## 210 0 0 0
## 211 0 0 0
## 212 0 0 0
## 213 0 0 0
## 214 0 0 0
## 215 0 0 0
## 216 0 1 0
## 217 0 0 0
## 218 0 0 0
## 219 0 0 0
## 220 0 0 0
## 221 0 0 0
## 222 0 0 0
## 223 0 0 0
## 224 0 0 0
## 225 0 0 0
## 226 0 0 0
## 227 0 0 0
## 228 0 0 0
## 229 0 0 0
## 230 0 0 0
## 231 0 0 0
## 232 0 0 0
## 233 0 0 0
## 234 0 0 0
## 235 0 0 1
## 236 0 0 0
## 237 0 0 0
## 238 0 0 0
## 239 0 0 0
## 240 0 0 0
## 241 0 0 0
## 242 0 0 0
## 243 0 0 0
## 244 0 0 0
## 245 0 0 0
## 246 0 0 0
## 247 0 0 0
## 248 0 0 0
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## 250 0 0 0
## 251 0 0 0
## 252 0 0 0
## 253 0 0 0
## 254 0 0 0
## 255 0 0 0
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## 260 0 0 0
## 261 0 0 0
## 262 0 0 0
## 263 0 0 0
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## 266 0 0 0
## 267 0 0 0
## 268 0 0 0
## 269 0 0 0
## 270 0 0 0
## 271 0 0 0
## 272 0 0 0
## 273 0 0 0
## 274 0 0 0
## 275 0 0 0
## 276 0 0 0
## 277 0 0 0
## Typ_nadwozia_Auta miejskie Typ_nadwozia_Coupe Typ_nadwozia_Kabriolet
## 1 1 0 0
## 2 0 0 0
## 3 0 0 0
## 4 1 0 0
## 5 0 0 0
## 6 0 0 0
## 7 0 0 0
## 8 0 0 0
## 9 0 0 0
## 10 0 0 0
## 11 0 0 0
## 12 0 0 0
## 13 1 0 0
## 14 0 0 0
## 15 0 0 0
## 16 0 0 0
## 17 0 0 0
## 18 0 0 0
## 19 0 0 0
## 20 0 0 0
## 21 0 0 0
## 22 1 0 0
## 23 0 0 0
## 24 1 0 0
## 25 0 0 0
## 26 0 0 0
## 27 0 0 0
## 28 0 0 0
## 29 0 0 0
## 30 1 0 0
## 31 1 0 0
## 32 0 0 0
## 33 0 0 0
## 34 0 0 0
## 35 0 0 0
## 36 0 0 0
## 37 0 0 0
## 38 0 0 0
## 39 0 0 0
## 40 0 0 0
## 41 0 0 0
## 42 0 0 0
## 43 0 0 0
## 44 1 0 0
## 45 0 0 0
## 46 0 0 0
## 47 0 0 0
## 48 0 0 0
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## 50 0 0 0
## 51 1 0 0
## 52 0 0 0
## 53 0 0 0
## 54 0 0 0
## 55 0 0 0
## 56 0 0 0
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## 59 0 0 0
## 60 0 0 0
## 61 0 0 0
## 62 0 0 0
## 63 0 0 0
## 64 0 0 0
## 65 0 0 0
## 66 0 0 0
## 67 1 0 0
## 68 0 0 0
## 69 0 0 0
## 70 0 0 0
## 71 0 0 0
## 72 0 0 0
## 73 0 0 0
## 74 0 0 0
## 75 0 0 0
## 76 0 0 0
## 77 1 0 0
## 78 0 1 0
## 79 0 0 0
## 80 0 0 0
## 81 0 0 0
## 82 0 0 0
## 83 0 0 0
## 84 0 0 0
## 85 0 0 0
## 86 0 0 0
## 87 0 0 0
## 88 0 0 0
## 89 0 0 0
## 90 0 0 0
## 91 0 0 0
## 92 0 0 0
## 93 0 0 0
## 94 0 0 0
## 95 0 0 0
## 96 0 0 0
## 97 0 0 0
## 98 0 0 0
## 99 0 0 0
## 100 0 0 0
## 101 0 0 0
## 102 0 0 0
## 103 0 0 0
## 104 0 0 0
## 105 0 0 0
## 106 0 0 0
## 107 0 0 0
## 108 0 0 0
## 109 0 0 0
## 110 0 0 0
## 111 0 0 0
## 112 0 0 0
## 113 1 0 0
## 114 0 0 0
## 115 1 0 0
## 116 0 0 0
## 117 0 0 0
## 118 0 0 0
## 119 0 0 0
## 120 0 0 0
## 121 0 0 0
## 122 0 0 0
## 123 0 0 0
## 124 1 0 0
## 125 0 1 0
## 126 0 0 0
## 127 0 0 0
## 128 0 0 0
## 129 0 0 0
## 130 0 0 0
## 131 0 0 0
## 132 1 0 0
## 133 0 0 0
## 134 0 0 0
## 135 0 0 1
## 136 0 0 0
## 137 0 0 0
## 138 0 0 0
## 139 0 0 0
## 140 0 0 0
## 141 0 0 0
## 142 0 0 0
## 143 0 0 0
## 144 1 0 0
## 145 0 0 0
## 146 0 0 0
## 147 0 0 0
## 148 0 0 0
## 149 0 0 0
## 150 0 0 0
## 151 0 0 0
## 152 0 0 0
## 153 0 0 0
## 154 0 1 0
## 155 0 1 0
## 156 0 0 0
## 157 0 0 0
## 158 0 0 1
## 159 0 0 0
## 160 0 0 0
## 161 0 1 0
## 162 0 0 0
## 163 0 0 0
## 164 0 0 0
## 165 0 0 0
## 166 0 0 0
## 167 0 0 0
## 168 0 0 0
## 169 1 0 0
## 170 0 0 0
## 171 1 0 0
## 172 1 0 0
## 173 0 0 0
## 174 0 0 0
## 175 0 0 0
## 176 0 0 0
## 177 0 0 0
## 178 0 0 0
## 179 0 0 0
## 180 0 0 0
## 181 0 0 0
## 182 0 0 0
## 183 0 0 0
## 184 0 0 0
## 185 0 0 0
## 186 0 0 0
## 187 0 0 0
## 188 0 0 1
## 189 0 1 0
## 190 1 0 0
## 191 0 0 0
## 192 0 0 0
## 193 0 0 0
## 194 0 0 0
## 195 0 0 0
## 196 1 0 0
## 197 0 0 0
## 198 0 0 0
## 199 0 0 0
## 200 0 0 0
## 201 0 0 0
## 202 0 0 0
## 203 0 0 0
## 204 0 0 0
## 205 0 0 0
## 206 0 0 0
## 207 0 0 0
## 208 0 0 0
## 209 0 0 0
## 210 0 0 0
## 211 1 0 0
## 212 0 0 0
## 213 1 0 0
## 214 0 0 0
## 215 0 0 0
## 216 0 0 0
## 217 0 0 0
## 218 0 0 0
## 219 0 0 0
## 220 0 0 0
## 221 0 0 0
## 222 0 0 0
## 223 0 0 0
## 224 0 0 0
## 225 0 1 0
## 226 0 0 0
## 227 0 0 0
## 228 0 0 0
## 229 0 0 0
## 230 0 0 0
## 231 0 0 0
## 232 0 0 0
## 233 0 0 0
## 234 0 0 0
## 235 0 0 0
## 236 0 0 0
## 237 0 0 0
## 238 0 0 0
## 239 0 0 0
## 240 0 0 0
## 241 0 0 0
## 242 0 0 0
## 243 0 0 0
## 244 0 0 0
## 245 0 0 0
## 246 0 0 0
## 247 0 0 0
## 248 0 0 0
## 249 0 0 0
## 250 1 0 0
## 251 0 0 0
## 252 0 0 0
## 253 0 0 0
## 254 0 0 0
## 255 0 0 0
## 256 0 0 0
## 257 0 0 0
## 258 0 0 0
## 259 0 0 0
## 260 0 0 0
## 261 0 0 0
## 262 0 0 0
## 263 0 0 0
## 264 0 0 0
## 265 0 0 0
## 266 0 0 0
## 267 0 0 0
## 268 0 0 0
## 269 1 0 0
## 270 0 0 0
## 271 0 0 0
## 272 0 0 0
## 273 1 0 0
## 274 1 0 0
## 275 0 0 0
## 276 1 0 0
## 277 0 0 0
## Typ_nadwozia_Kombi Typ_nadwozia_Kompakt Typ_nadwozia_Minivan
## 1 0 0 0
## 2 1 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 1 0
## 6 0 0 1
## 7 0 0 0
## 8 0 0 0
## 9 1 0 0
## 10 0 0 0
## 11 1 0 0
## 12 0 1 0
## 13 0 0 0
## 14 0 0 0
## 15 0 0 0
## 16 0 1 0
## 17 1 0 0
## 18 0 0 0
## 19 0 1 0
## 20 0 0 0
## 21 0 0 0
## 22 0 0 0
## 23 0 0 0
## 24 0 0 0
## 25 1 0 0
## 26 0 0 1
## 27 1 0 0
## 28 0 0 0
## 29 0 0 0
## 30 0 0 0
## 31 0 0 0
## 32 0 1 0
## 33 1 0 0
## 34 0 0 1
## 35 0 0 1
## 36 0 0 0
## 37 0 1 0
## 38 1 0 0
## 39 0 0 0
## 40 0 0 1
## 41 1 0 0
## 42 0 0 0
## 43 1 0 0
## 44 0 0 0
## 45 0 0 0
## 46 0 0 0
## 47 0 0 1
## 48 0 0 0
## 49 1 0 0
## 50 0 0 1
## 51 0 0 0
## 52 1 0 0
## 53 0 1 0
## 54 0 0 0
## 55 0 1 0
## 56 0 0 0
## 57 0 0 0
## 58 0 0 0
## 59 0 0 0
## 60 0 1 0
## 61 0 1 0
## 62 0 0 0
## 63 0 1 0
## 64 0 0 1
## 65 0 0 1
## 66 1 0 0
## 67 0 0 0
## 68 0 0 0
## 69 1 0 0
## 70 0 0 0
## 71 0 0 1
## 72 0 0 0
## 73 0 0 0
## 74 0 0 0
## 75 0 0 0
## 76 0 0 0
## 77 0 0 0
## 78 0 0 0
## 79 0 0 0
## 80 0 0 0
## 81 0 0 0
## 82 0 0 0
## 83 0 1 0
## 84 0 1 0
## 85 0 0 0
## 86 0 0 0
## 87 0 1 0
## 88 0 0 0
## 89 0 1 0
## 90 1 0 0
## 91 1 0 0
## 92 0 0 0
## 93 0 0 0
## 94 1 0 0
## 95 0 0 0
## 96 0 0 0
## 97 1 0 0
## 98 0 1 0
## 99 0 0 0
## 100 1 0 0
## 101 0 1 0
## 102 0 0 0
## 103 0 1 0
## 104 0 0 0
## 105 1 0 0
## 106 0 0 0
## 107 0 0 1
## 108 0 0 0
## 109 0 1 0
## 110 1 0 0
## 111 0 0 0
## 112 0 0 0
## 113 0 0 0
## 114 0 0 1
## 115 0 0 0
## 116 0 0 1
## 117 0 0 0
## 118 0 1 0
## 119 1 0 0
## 120 0 0 0
## 121 0 0 0
## 122 0 0 0
## 123 0 0 1
## 124 0 0 0
## 125 0 0 0
## 126 1 0 0
## 127 0 1 0
## 128 0 0 0
## 129 1 0 0
## 130 0 0 0
## 131 1 0 0
## 132 0 0 0
## 133 0 0 0
## 134 1 0 0
## 135 0 0 0
## 136 0 1 0
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## 138 0 1 0
## 139 0 0 0
## 140 0 0 0
## 141 0 0 0
## 142 1 0 0
## 143 1 0 0
## 144 0 0 0
## 145 0 0 0
## 146 0 1 0
## 147 0 0 0
## 148 0 0 1
## 149 0 0 0
## 150 1 0 0
## 151 0 0 0
## 152 0 0 0
## 153 0 1 0
## 154 0 0 0
## 155 0 0 0
## 156 0 0 0
## 157 0 0 0
## 158 0 0 0
## 159 0 1 0
## 160 0 0 0
## 161 0 0 0
## 162 0 0 0
## 163 1 0 0
## 164 0 1 0
## 165 0 0 0
## 166 0 0 0
## 167 1 0 0
## 168 0 0 0
## 169 0 0 0
## 170 0 1 0
## 171 0 0 0
## 172 0 0 0
## 173 1 0 0
## 174 0 0 1
## 175 1 0 0
## 176 1 0 0
## 177 0 1 0
## 178 0 0 0
## 179 0 0 0
## 180 0 1 0
## 181 0 0 0
## 182 1 0 0
## 183 0 0 0
## 184 0 0 1
## 185 0 0 0
## 186 0 0 0
## 187 0 1 0
## 188 0 0 0
## 189 0 0 0
## 190 0 0 0
## 191 0 0 0
## 192 0 0 0
## 193 0 0 0
## 194 1 0 0
## 195 1 0 0
## 196 0 0 0
## 197 0 0 0
## 198 1 0 0
## 199 1 0 0
## 200 1 0 0
## 201 0 1 0
## 202 0 0 0
## 203 0 0 0
## 204 0 0 0
## 205 1 0 0
## 206 0 0 0
## 207 0 0 1
## 208 0 0 0
## 209 0 1 0
## 210 0 0 0
## 211 0 0 0
## 212 1 0 0
## 213 0 0 0
## 214 0 0 0
## 215 0 0 0
## 216 0 0 0
## 217 0 1 0
## 218 1 0 0
## 219 1 0 0
## 220 1 0 0
## 221 0 1 0
## 222 0 0 0
## 223 0 0 0
## 224 0 0 0
## 225 0 0 0
## 226 0 1 0
## 227 0 0 1
## 228 0 0 0
## 229 0 0 0
## 230 0 1 0
## 231 0 1 0
## 232 0 0 1
## 233 0 1 0
## 234 0 1 0
## 235 0 0 0
## 236 0 0 0
## 237 1 0 0
## 238 1 0 0
## 239 0 0 0
## 240 0 1 0
## 241 0 1 0
## 242 0 0 1
## 243 0 0 0
## 244 1 0 0
## 245 1 0 0
## 246 0 0 0
## 247 1 0 0
## 248 1 0 0
## 249 1 0 0
## 250 0 0 0
## 251 1 0 0
## 252 0 0 0
## 253 0 0 0
## 254 1 0 0
## 255 1 0 0
## 256 1 0 0
## 257 0 0 0
## 258 0 0 0
## 259 0 1 0
## 260 0 1 0
## 261 1 0 0
## 262 0 0 0
## 263 0 0 0
## 264 0 0 0
## 265 0 0 0
## 266 1 0 0
## 267 0 0 0
## 268 1 0 0
## 269 0 0 0
## 270 1 0 0
## 271 1 0 0
## 272 1 0 0
## 273 0 0 0
## 274 0 0 0
## 275 0 0 0
## 276 0 0 0
## 277 0 0 0
## Typ_nadwozia_Sedan Typ_nadwozia_SUV Kolor_Beżowy Kolor_Biały Kolor_Błękitny
## 1 0 0 0 0 0
## 2 0 0 0 0 0
## 3 0 1 1 0 0
## 4 0 0 0 0 0
## 5 0 0 0 0 0
## 6 0 0 0 0 0
## 7 0 1 0 0 0
## 8 1 0 0 0 0
## 9 0 0 0 0 0
## 10 0 1 0 0 0
## 11 0 0 0 0 0
## 12 0 0 0 1 0
## 13 0 0 1 0 0
## 14 1 0 0 0 0
## 15 1 0 0 0 0
## 16 0 0 0 0 0
## 17 0 0 0 0 0
## 18 0 1 0 1 0
## 19 0 0 1 0 0
## 20 0 1 0 1 0
## 21 0 1 0 0 0
## 22 0 0 0 0 0
## 23 0 0 0 0 0
## 24 0 0 0 0 0
## 25 0 0 0 0 0
## 26 0 0 0 0 0
## 27 0 0 0 0 0
## 28 0 1 0 1 0
## 29 1 0 0 0 0
## 30 0 0 0 0 0
## 31 0 0 0 0 0
## 32 0 0 0 0 0
## 33 0 0 0 0 0
## 34 0 0 0 0 0
## 35 0 0 0 1 0
## 36 0 1 0 0 0
## 37 0 0 0 1 0
## 38 0 0 0 0 0
## 39 0 1 0 0 0
## 40 0 0 0 0 0
## 41 0 0 0 0 0
## 42 1 0 0 0 0
## 43 0 0 0 1 0
## 44 0 0 0 0 0
## 45 1 0 0 0 0
## 46 0 1 0 0 0
## 47 0 0 0 0 0
## 48 0 1 0 1 0
## 49 0 0 0 0 0
## 50 0 0 0 0 0
## 51 0 0 0 0 0
## 52 0 0 0 0 0
## 53 0 0 0 0 0
## 54 1 0 0 0 0
## 55 0 0 0 0 0
## 56 1 0 0 0 0
## 57 0 1 0 0 0
## 58 0 1 0 0 0
## 59 1 0 0 0 0
## 60 0 0 0 0 0
## 61 0 0 0 0 0
## 62 0 1 0 0 0
## 63 0 0 0 0 0
## 64 0 0 0 0 0
## 65 0 0 0 0 0
## 66 0 0 0 0 0
## 67 0 0 0 0 0
## 68 1 0 0 0 0
## 69 0 0 0 0 0
## 70 1 0 0 0 0
## 71 0 0 0 0 0
## 72 0 1 0 0 0
## 73 1 0 0 0 0
## 74 0 1 0 0 0
## 75 1 0 0 0 0
## 76 1 0 0 0 0
## 77 0 0 0 0 0
## 78 0 0 0 0 0
## 79 0 1 0 0 0
## 80 1 0 0 0 0
## 81 1 0 0 1 0
## 82 1 0 0 0 0
## 83 0 0 0 0 0
## 84 0 0 0 0 0
## 85 0 1 0 0 0
## 86 1 0 0 0 0
## 87 0 0 0 0 0
## 88 0 1 0 1 0
## 89 0 0 0 0 0
## 90 0 0 0 0 0
## 91 0 0 0 0 0
## 92 0 0 0 1 0
## 93 0 1 0 1 0
## 94 0 0 0 0 0
## 95 1 0 0 0 0
## 96 0 1 0 0 0
## 97 0 0 0 0 0
## 98 0 0 0 0 0
## 99 0 1 0 0 0
## 100 0 0 1 0 0
## 101 0 0 0 0 0
## 102 0 1 0 0 0
## 103 0 0 0 0 0
## 104 1 0 0 0 0
## 105 0 0 0 0 0
## 106 0 1 0 0 0
## 107 0 0 0 0 0
## 108 0 1 0 0 0
## 109 0 0 0 0 0
## 110 0 0 0 0 0
## 111 0 1 0 0 0
## 112 1 0 0 0 0
## 113 0 0 0 0 0
## 114 0 0 0 0 0
## 115 0 0 0 0 0
## 116 0 0 0 0 0
## 117 0 1 0 0 0
## 118 0 0 0 0 0
## 119 0 0 0 1 0
## 120 0 1 0 1 0
## 121 1 0 0 0 0
## 122 1 0 0 0 0
## 123 0 0 0 0 0
## 124 0 0 0 0 0
## 125 0 0 0 0 0
## 126 0 0 0 0 0
## 127 0 0 0 0 0
## 128 0 1 0 0 0
## 129 0 0 0 0 0
## 130 0 1 0 0 0
## 131 0 0 0 0 0
## 132 0 0 0 0 0
## 133 0 1 0 0 0
## 134 0 0 0 0 0
## 135 0 0 0 0 0
## 136 0 0 0 0 0
## 137 0 0 0 0 0
## 138 0 0 0 0 0
## 139 0 1 0 0 0
## 140 1 0 0 0 0
## 141 0 0 0 0 0
## 142 0 0 0 0 0
## 143 0 0 0 0 0
## 144 0 0 0 0 0
## 145 0 1 0 0 0
## 146 0 0 0 0 0
## 147 1 0 0 0 0
## 148 0 0 0 0 0
## 149 0 1 0 1 0
## 150 0 0 0 0 0
## 151 1 0 0 0 0
## 152 0 0 0 0 0
## 153 0 0 0 0 0
## 154 0 0 0 0 0
## 155 0 0 0 0 0
## 156 1 0 0 0 0
## 157 0 1 0 0 0
## 158 0 0 0 1 0
## 159 0 0 0 0 0
## 160 0 1 0 0 0
## 161 0 0 0 0 0
## 162 1 0 0 0 0
## 163 0 0 0 1 0
## 164 0 0 0 1 0
## 165 0 1 0 0 0
## 166 1 0 0 0 0
## 167 0 0 0 1 0
## 168 1 0 0 0 0
## 169 0 0 0 1 0
## 170 0 0 0 0 0
## 171 0 0 0 0 0
## 172 0 0 0 0 0
## 173 0 0 0 0 0
## 174 0 0 0 0 0
## 175 0 0 0 0 0
## 176 0 0 0 0 0
## 177 0 0 0 0 0
## 178 0 0 0 0 0
## 179 0 0 0 0 0
## 180 0 0 0 0 0
## 181 0 1 0 0 0
## 182 0 0 0 1 0
## 183 1 0 0 0 0
## 184 0 0 0 0 0
## 185 0 1 0 0 0
## 186 0 1 0 0 0
## 187 0 0 0 0 0
## 188 0 0 0 0 0
## 189 0 0 0 0 0
## 190 0 0 0 1 0
## 191 1 0 0 0 0
## 192 1 0 0 0 0
## 193 0 1 0 0 0
## 194 0 0 0 0 0
## 195 0 0 0 0 0
## 196 0 0 0 0 0
## 197 0 1 0 0 0
## 198 0 0 0 0 0
## 199 0 0 0 0 0
## 200 0 0 0 0 0
## 201 0 0 0 0 0
## 202 1 0 0 0 0
## 203 0 1 1 0 0
## 204 1 0 0 0 0
## 205 0 0 0 0 0
## 206 0 0 0 0 0
## 207 0 0 0 0 1
## 208 0 1 0 0 0
## 209 0 0 0 0 0
## 210 1 0 0 0 0
## 211 0 0 0 0 0
## 212 0 0 0 0 0
## 213 0 0 0 0 0
## 214 1 0 0 0 0
## 215 0 1 0 0 0
## 216 0 1 0 0 0
## 217 0 0 0 1 0
## 218 0 0 0 1 0
## 219 0 0 0 0 0
## 220 0 0 0 0 0
## 221 0 0 0 0 0
## 222 0 1 0 1 0
## 223 0 1 0 0 0
## 224 0 1 0 1 0
## 225 0 0 0 0 0
## 226 0 0 0 0 0
## 227 0 0 0 1 0
## 228 0 1 0 1 0
## 229 1 0 0 0 0
## 230 0 0 0 0 0
## 231 0 0 0 0 0
## 232 0 0 0 0 0
## 233 0 0 0 0 0
## 234 0 0 0 0 0
## 235 0 0 0 0 0
## 236 0 1 0 0 0
## 237 0 0 0 0 0
## 238 0 0 0 0 0
## 239 0 1 0 0 0
## 240 0 0 0 0 0
## 241 0 0 0 0 0
## 242 0 0 0 0 0
## 243 0 1 0 0 0
## 244 0 0 0 0 0
## 245 0 0 0 0 0
## 246 0 1 0 0 0
## 247 0 0 0 0 0
## 248 0 0 0 0 0
## 249 0 0 0 0 0
## 250 0 0 0 0 0
## 251 0 0 0 1 0
## 252 0 1 0 1 0
## 253 0 1 0 1 0
## 254 0 0 0 0 0
## 255 0 0 0 0 0
## 256 0 0 0 0 0
## 257 0 1 0 0 0
## 258 1 0 0 0 0
## 259 0 0 0 0 0
## 260 0 0 0 1 0
## 261 0 0 0 0 0
## 262 0 1 0 0 0
## 263 1 0 0 0 0
## 264 0 1 0 0 0
## 265 1 0 0 0 0
## 266 0 0 0 1 0
## 267 0 1 0 0 0
## 268 0 0 0 0 0
## 269 0 0 0 0 0
## 270 0 0 0 0 0
## 271 0 0 0 0 0
## 272 0 0 0 1 0
## 273 0 0 0 1 0
## 274 0 0 0 1 0
## 275 0 1 0 0 0
## 276 0 0 0 0 0
## 277 0 1 0 1 0
## Kolor_Bordowy Kolor_Brązowy Kolor_Czarny Kolor_Czerwony Kolor_Fioletowy
## 1 0 0 1 0 0
## 2 0 0 0 0 0
## 3 0 0 0 0 0
## 4 0 0 0 0 0
## 5 0 0 0 0 0
## 6 0 0 0 0 0
## 7 0 0 0 0 0
## 8 0 0 0 0 0
## 9 0 0 0 0 0
## 10 0 0 0 1 0
## 11 0 0 0 0 0
## 12 0 0 0 0 0
## 13 0 0 0 0 0
## 14 0 0 0 0 0
## 15 1 0 0 0 0
## 16 0 0 0 0 0
## 17 0 1 0 0 0
## 18 0 0 0 0 0
## 19 0 0 0 0 0
## 20 0 0 0 0 0
## 21 0 0 1 0 0
## 22 0 0 1 0 0
## 23 0 0 1 0 0
## 24 0 0 0 0 0
## 25 0 0 1 0 0
## 26 0 0 0 0 0
## 27 0 1 0 0 0
## 28 0 0 0 0 0
## 29 0 0 0 0 0
## 30 0 0 0 0 0
## 31 0 0 0 0 0
## 32 0 0 0 1 0
## 33 0 0 0 0 0
## 34 0 1 0 0 0
## 35 0 0 0 0 0
## 36 0 0 1 0 0
## 37 0 0 0 0 0
## 38 0 0 0 0 0
## 39 0 0 1 0 0
## 40 0 0 0 0 0
## 41 0 0 0 0 0
## 42 0 0 1 0 0
## 43 0 0 0 0 0
## 44 0 0 0 0 0
## 45 0 0 0 0 0
## 46 0 0 1 0 0
## 47 0 0 0 0 0
## 48 0 0 0 0 0
## 49 0 0 0 0 0
## 50 0 0 0 0 0
## 51 0 0 0 0 0
## 52 0 0 0 0 0
## 53 0 0 1 0 0
## 54 0 0 1 0 0
## 55 0 0 1 0 0
## 56 0 0 1 0 0
## 57 0 0 1 0 0
## 58 0 0 0 0 0
## 59 0 0 0 0 0
## 60 0 0 1 0 0
## 61 0 0 0 0 0
## 62 0 0 0 0 0
## 63 0 0 0 0 0
## 64 0 0 0 0 0
## 65 0 0 0 0 0
## 66 0 0 0 0 0
## 67 0 0 0 0 0
## 68 0 0 1 0 0
## 69 0 0 0 0 0
## 70 0 0 1 0 0
## 71 0 0 0 0 0
## 72 0 0 1 0 0
## 73 0 0 0 0 0
## 74 0 0 1 0 0
## 75 0 0 1 0 0
## 76 0 0 0 0 0
## 77 0 0 0 0 0
## 78 0 0 0 0 0
## 79 0 0 0 0 0
## 80 0 0 0 0 0
## 81 0 0 0 0 0
## 82 0 0 0 0 0
## 83 0 1 0 0 0
## 84 0 0 0 1 0
## 85 0 0 0 0 0
## 86 0 0 0 0 0
## 87 0 0 0 0 0
## 88 0 0 0 0 0
## 89 0 0 1 0 0
## 90 0 0 1 0 0
## 91 0 0 0 0 0
## 92 0 0 0 0 0
## 93 0 0 0 0 0
## 94 0 0 0 0 0
## 95 0 0 0 0 0
## 96 0 0 0 0 0
## 97 0 0 0 0 0
## 98 0 1 0 0 0
## 99 0 0 0 0 0
## 100 0 0 0 0 0
## 101 0 0 0 1 0
## 102 0 0 0 0 0
## 103 0 0 0 0 0
## 104 0 0 0 0 0
## 105 0 0 1 0 0
## 106 0 0 0 0 0
## 107 0 0 1 0 0
## 108 0 0 0 0 0
## 109 0 0 0 1 0
## 110 0 0 1 0 0
## 111 0 0 1 0 0
## 112 1 0 0 0 0
## 113 0 0 0 0 0
## 114 0 0 1 0 0
## 115 0 0 0 0 0
## 116 0 0 0 0 0
## 117 0 0 0 0 0
## 118 0 0 0 0 0
## 119 0 0 0 0 0
## 120 0 0 0 0 0
## 121 0 0 1 0 0
## 122 0 0 0 0 0
## 123 0 0 0 1 0
## 124 0 0 1 0 0
## 125 0 0 1 0 0
## 126 0 0 0 0 0
## 127 1 0 0 0 0
## 128 0 0 0 0 0
## 129 0 0 0 0 0
## 130 0 0 0 0 0
## 131 0 0 0 0 0
## 132 0 0 0 0 0
## 133 0 0 0 0 0
## 134 0 0 1 0 0
## 135 0 0 1 0 0
## 136 0 0 1 0 0
## 137 0 0 0 0 0
## 138 0 0 0 0 0
## 139 0 0 0 0 0
## 140 0 0 0 0 0
## 141 1 0 0 0 0
## 142 0 0 0 0 0
## 143 0 0 1 0 0
## 144 0 0 0 0 0
## 145 0 0 0 0 0
## 146 0 0 1 0 0
## 147 0 0 0 0 0
## 148 0 0 0 0 0
## 149 0 0 0 0 0
## 150 0 0 1 0 0
## 151 0 0 0 0 0
## 152 0 0 0 1 0
## 153 0 0 0 0 0
## 154 0 0 0 0 0
## 155 0 0 0 0 0
## 156 0 0 0 0 0
## 157 0 0 0 0 0
## 158 0 0 0 0 0
## 159 0 0 0 0 0
## 160 0 0 0 0 0
## 161 0 0 0 0 0
## 162 0 0 0 0 0
## 163 0 0 0 0 0
## 164 0 0 0 0 0
## 165 0 0 0 0 0
## 166 0 0 0 0 0
## 167 0 0 0 0 0
## 168 0 0 0 0 0
## 169 0 0 0 0 0
## 170 0 0 0 1 0
## 171 0 0 0 0 0
## 172 0 0 0 0 0
## 173 0 0 0 0 0
## 174 0 0 1 0 0
## 175 0 0 0 0 0
## 176 0 0 1 0 0
## 177 0 0 0 0 0
## 178 0 0 1 0 0
## 179 0 0 0 0 0
## 180 0 0 0 1 0
## 181 0 0 0 0 0
## 182 0 0 0 0 0
## 183 0 0 0 0 0
## 184 0 0 0 0 0
## 185 0 0 0 0 0
## 186 0 0 1 0 0
## 187 0 0 0 1 0
## 188 0 0 1 0 0
## 189 0 0 1 0 0
## 190 0 0 0 0 0
## 191 0 0 1 0 0
## 192 0 0 1 0 0
## 193 0 0 0 0 0
## 194 0 0 0 0 0
## 195 0 0 0 0 0
## 196 0 0 0 1 0
## 197 0 0 1 0 0
## 198 0 0 0 0 0
## 199 0 0 1 0 0
## 200 0 0 1 0 0
## 201 0 0 0 0 0
## 202 0 0 0 0 0
## 203 0 0 0 0 0
## 204 0 0 1 0 0
## 205 0 0 1 0 0
## 206 0 0 1 0 0
## 207 0 0 0 0 0
## 208 0 0 0 0 0
## 209 0 1 0 0 0
## 210 0 0 0 0 0
## 211 0 0 0 0 0
## 212 0 0 1 0 0
## 213 0 0 0 0 0
## 214 0 0 0 0 0
## 215 0 1 0 0 0
## 216 0 1 0 0 0
## 217 0 0 0 0 0
## 218 0 0 0 0 0
## 219 0 0 0 0 0
## 220 0 0 0 0 0
## 221 0 0 0 0 0
## 222 0 0 0 0 0
## 223 0 1 0 0 0
## 224 0 0 0 0 0
## 225 0 0 0 0 0
## 226 0 0 0 0 0
## 227 0 0 0 0 0
## 228 0 0 0 0 0
## 229 0 0 0 0 0
## 230 0 0 0 0 0
## 231 0 0 0 0 0
## 232 0 0 0 0 0
## 233 0 0 0 0 0
## 234 0 0 0 1 0
## 235 0 0 1 0 0
## 236 0 0 0 0 0
## 237 0 0 1 0 0
## 238 1 0 0 0 0
## 239 0 0 0 1 0
## 240 0 0 1 0 0
## 241 0 0 1 0 0
## 242 0 0 0 0 0
## 243 0 0 1 0 0
## 244 0 0 1 0 0
## 245 0 0 1 0 0
## 246 0 1 0 0 0
## 247 0 0 1 0 0
## 248 0 0 1 0 0
## 249 0 0 1 0 0
## 250 0 0 1 0 0
## 251 0 0 0 0 0
## 252 0 0 0 0 0
## 253 0 0 0 0 0
## 254 0 0 0 0 0
## 255 0 0 1 0 0
## 256 0 1 0 0 0
## 257 0 0 1 0 0
## 258 0 0 0 0 0
## 259 0 0 0 0 0
## 260 0 0 0 0 0
## 261 0 0 1 0 0
## 262 0 0 1 0 0
## 263 0 0 1 0 0
## 264 0 0 0 0 0
## 265 0 0 0 1 0
## 266 0 0 0 0 0
## 267 0 0 0 0 0
## 268 0 0 0 0 0
## 269 0 0 0 0 0
## 270 0 0 0 0 0
## 271 0 0 0 0 0
## 272 0 0 0 0 0
## 273 0 0 0 0 0
## 274 0 0 0 0 0
## 275 0 0 1 0 0
## 276 1 0 0 0 0
## 277 0 0 0 0 0
## Kolor_Granatowy Kolor_Inny kolor Kolor_Niebieski Kolor_Pomarańczowy
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 1 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## 7 0 0 0 0
## 8 1 0 0 0
## 9 0 1 0 0
## 10 0 0 0 0
## 11 0 1 0 0
## 12 0 0 0 0
## 13 0 0 0 0
## 14 0 1 0 0
## 15 0 0 0 0
## 16 0 0 0 0
## 17 0 0 0 0
## 18 0 0 0 0
## 19 0 0 0 0
## 20 0 0 0 0
## 21 0 0 0 0
## 22 0 0 0 0
## 23 0 0 0 0
## 24 0 0 1 0
## 25 0 0 0 0
## 26 0 0 0 0
## 27 0 0 0 0
## 28 0 0 0 0
## 29 0 0 0 0
## 30 0 0 0 0
## 31 0 0 1 0
## 32 0 0 0 0
## 33 0 0 0 0
## 34 0 0 0 0
## 35 0 0 0 0
## 36 0 0 0 0
## 37 0 0 0 0
## 38 0 0 0 0
## 39 0 0 0 0
## 40 0 0 0 0
## 41 0 0 0 0
## 42 0 0 0 0
## 43 0 0 0 0
## 44 0 0 0 0
## 45 1 0 0 0
## 46 0 0 0 0
## 47 0 0 0 0
## 48 0 0 0 0
## 49 0 0 1 0
## 50 0 0 0 0
## 51 0 0 0 0
## 52 0 0 0 0
## 53 0 0 0 0
## 54 0 0 0 0
## 55 0 0 0 0
## 56 0 0 0 0
## 57 0 0 0 0
## 58 0 1 0 0
## 59 0 0 1 0
## 60 0 0 0 0
## 61 0 0 0 0
## 62 0 0 0 0
## 63 0 0 0 0
## 64 0 0 0 0
## 65 0 0 0 0
## 66 0 0 1 0
## 67 0 0 0 0
## 68 0 0 0 0
## 69 0 0 0 0
## 70 0 0 0 0
## 71 0 1 0 0
## 72 0 0 0 0
## 73 0 0 0 0
## 74 0 0 0 0
## 75 0 0 0 0
## 76 1 0 0 0
## 77 1 0 0 0
## 78 0 0 1 0
## 79 0 0 1 0
## 80 0 1 0 0
## 81 0 0 0 0
## 82 1 0 0 0
## 83 0 0 0 0
## 84 0 0 0 0
## 85 0 0 0 0
## 86 0 0 0 0
## 87 0 0 0 0
## 88 0 0 0 0
## 89 0 0 0 0
## 90 0 0 0 0
## 91 0 0 0 0
## 92 0 0 0 0
## 93 0 0 0 0
## 94 0 1 0 0
## 95 0 0 0 0
## 96 0 0 0 0
## 97 0 0 0 0
## 98 0 0 0 0
## 99 0 0 1 0
## 100 0 0 0 0
## 101 0 0 0 0
## 102 1 0 0 0
## 103 0 0 1 0
## 104 0 0 1 0
## 105 0 0 0 0
## 106 0 1 0 0
## 107 0 0 0 0
## 108 0 0 0 0
## 109 0 0 0 0
## 110 0 0 0 0
## 111 0 0 0 0
## 112 0 0 0 0
## 113 0 0 0 0
## 114 0 0 0 0
## 115 0 0 1 0
## 116 0 0 1 0
## 117 0 0 0 0
## 118 0 0 0 0
## 119 0 0 0 0
## 120 0 0 0 0
## 121 0 0 0 0
## 122 0 0 0 0
## 123 0 0 0 0
## 124 0 0 0 0
## 125 0 0 0 0
## 126 0 0 0 0
## 127 0 0 0 0
## 128 0 0 0 0
## 129 0 0 0 0
## 130 0 0 0 0
## 131 0 0 0 0
## 132 0 0 1 0
## 133 0 0 0 0
## 134 0 0 0 0
## 135 0 0 0 0
## 136 0 0 0 0
## 137 0 0 0 0
## 138 0 0 0 0
## 139 0 0 0 0
## 140 0 0 1 0
## 141 0 0 0 0
## 142 0 0 0 0
## 143 0 0 0 0
## 144 0 0 1 0
## 145 0 0 0 0
## 146 0 0 0 0
## 147 0 0 0 0
## 148 0 0 0 0
## 149 0 0 0 0
## 150 0 0 0 0
## 151 0 0 0 0
## 152 0 0 0 0
## 153 0 0 0 0
## 154 0 0 1 0
## 155 0 0 1 0
## 156 0 0 0 0
## 157 0 0 0 0
## 158 0 0 0 0
## 159 1 0 0 0
## 160 0 0 0 0
## 161 1 0 0 0
## 162 1 0 0 0
## 163 0 0 0 0
## 164 0 0 0 0
## 165 0 0 0 0
## 166 0 0 0 0
## 167 0 0 0 0
## 168 0 0 0 0
## 169 0 0 0 0
## 170 0 0 0 0
## 171 1 0 0 0
## 172 0 0 0 0
## 173 0 0 0 0
## 174 0 0 0 0
## 175 0 0 0 0
## 176 0 0 0 0
## 177 0 0 0 0
## 178 0 0 0 0
## 179 0 0 0 1
## 180 0 0 0 0
## 181 0 0 0 0
## 182 0 0 0 0
## 183 0 0 0 0
## 184 0 0 0 0
## 185 0 0 0 0
## 186 0 0 0 0
## 187 0 0 0 0
## 188 0 0 0 0
## 189 0 0 0 0
## 190 0 0 0 0
## 191 0 0 0 0
## 192 0 0 0 0
## 193 0 0 0 0
## 194 0 0 0 0
## 195 0 0 0 0
## 196 0 0 0 0
## 197 0 0 0 0
## 198 0 0 0 0
## 199 0 0 0 0
## 200 0 0 0 0
## 201 0 0 1 0
## 202 0 0 0 0
## 203 0 0 0 0
## 204 0 0 0 0
## 205 0 0 0 0
## 206 0 0 0 0
## 207 0 0 0 0
## 208 0 0 0 0
## 209 0 0 0 0
## 210 0 1 0 0
## 211 0 0 0 0
## 212 0 0 0 0
## 213 0 0 0 0
## 214 0 1 0 0
## 215 0 0 0 0
## 216 0 0 0 0
## 217 0 0 0 0
## 218 0 0 0 0
## 219 0 0 1 0
## 220 0 0 0 0
## 221 0 0 1 0
## 222 0 0 0 0
## 223 0 0 0 0
## 224 0 0 0 0
## 225 0 1 0 0
## 226 0 0 0 0
## 227 0 0 0 0
## 228 0 0 0 0
## 229 0 0 0 0
## 230 0 0 1 0
## 231 0 0 0 0
## 232 0 1 0 0
## 233 0 1 0 0
## 234 0 0 0 0
## 235 0 0 0 0
## 236 0 0 0 0
## 237 0 0 0 0
## 238 0 0 0 0
## 239 0 0 0 0
## 240 0 0 0 0
## 241 0 0 0 0
## 242 0 0 0 0
## 243 0 0 0 0
## 244 0 0 0 0
## 245 0 0 0 0
## 246 0 0 0 0
## 247 0 0 0 0
## 248 0 0 0 0
## 249 0 0 0 0
## 250 0 0 0 0
## 251 0 0 0 0
## 252 0 0 0 0
## 253 0 0 0 0
## 254 0 0 0 0
## 255 0 0 0 0
## 256 0 0 0 0
## 257 0 0 0 0
## 258 0 0 0 0
## 259 0 0 0 0
## 260 0 0 0 0
## 261 0 0 0 0
## 262 0 0 0 0
## 263 0 0 0 0
## 264 0 0 1 0
## 265 0 0 0 0
## 266 0 0 0 0
## 267 0 1 0 0
## 268 0 0 0 0
## 269 0 0 0 0
## 270 0 0 1 0
## 271 0 0 0 0
## 272 0 0 0 0
## 273 0 0 0 0
## 274 0 0 0 0
## 275 0 0 0 0
## 276 0 0 0 0
## 277 0 0 0 0
## Kolor_Srebrny Kolor_Szary Kolor_Zielony Kolor_Złoty Kolor_Żółty
## 1 0 0 0 0 0
## 2 0 1 0 0 0
## 3 0 0 0 0 0
## 4 0 0 0 0 0
## 5 0 1 0 0 0
## 6 1 0 0 0 0
## 7 1 0 0 0 0
## 8 0 0 0 0 0
## 9 0 0 0 0 0
## 10 0 0 0 0 0
## 11 0 0 0 0 0
## 12 0 0 0 0 0
## 13 0 0 0 0 0
## 14 0 0 0 0 0
## 15 0 0 0 0 0
## 16 0 1 0 0 0
## 17 0 0 0 0 0
## 18 0 0 0 0 0
## 19 0 0 0 0 0
## 20 0 0 0 0 0
## 21 0 0 0 0 0
## 22 0 0 0 0 0
## 23 0 0 0 0 0
## 24 0 0 0 0 0
## 25 0 0 0 0 0
## 26 0 1 0 0 0
## 27 0 0 0 0 0
## 28 0 0 0 0 0
## 29 0 1 0 0 0
## 30 0 1 0 0 0
## 31 0 0 0 0 0
## 32 0 0 0 0 0
## 33 0 1 0 0 0
## 34 0 0 0 0 0
## 35 0 0 0 0 0
## 36 0 0 0 0 0
## 37 0 0 0 0 0
## 38 0 0 1 0 0
## 39 0 0 0 0 0
## 40 0 1 0 0 0
## 41 0 1 0 0 0
## 42 0 0 0 0 0
## 43 0 0 0 0 0
## 44 1 0 0 0 0
## 45 0 0 0 0 0
## 46 0 0 0 0 0
## 47 1 0 0 0 0
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## [ reached 'max' / getOption("max.print") -- omitted 971 rows ]autka_dt_no_power <- subset(autka_dt,select = -4)
autka_dt_no_power## Rok_produkcji Przebieg Pojemność_skokowa Cena Marka_pojazdu_Alfa Romeo
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## Model_pojazdu_3008 Model_pojazdu_300C Model_pojazdu_300s Model_pojazdu_301
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## Model_pojazdu_307 Model_pojazdu_308 Model_pojazdu_370 Z Model_pojazdu_3GT
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## Model_pojazdu_407 Model_pojazdu_5 Model_pojazdu_500 Model_pojazdu_500X
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## Model_pojazdu_508 Model_pojazdu_6 Model_pojazdu_626
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## Model_pojazdu_718 Boxster Model_pojazdu_718 Cayman Model_pojazdu_80
## 1 0 0 0
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## Model_pojazdu_9-3 Model_pojazdu_911 Model_pojazdu_A1 Model_pojazdu_A3
## 1 0 0 0 0
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## Model_pojazdu_A4 Model_pojazdu_A5 Model_pojazdu_A6 Model_pojazdu_A6 Allroad
## 1 0 0 0 0
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## 247 0 0 0 0
## Model_pojazdu_A7 Model_pojazdu_A8 Model_pojazdu_Accord Model_pojazdu_Adam
## 1 0 0 0 0
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## Model_pojazdu_Altea Model_pojazdu_Altea XL Model_pojazdu_AMG GT
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## Model_pojazdu_Antara Model_pojazdu_Arteon Model_pojazdu_Astra
## 1 0 0 0
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## Model_pojazdu_ASX Model_pojazdu_Ateca Model_pojazdu_Auris
## 1 0 0 0
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## Model_pojazdu_Avensis Model_pojazdu_Aveo Model_pojazdu_Aygo
## 1 0 0 0
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## Model_pojazdu_Baleno Model_pojazdu_Berlingo Model_pojazdu_BLS
## 1 0 0 0
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## Model_pojazdu_Bravo Model_pojazdu_C-HR Model_pojazdu_C-MAX Model_pojazdu_C1
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## Model_pojazdu_C3 Model_pojazdu_C3 Picasso Model_pojazdu_C30
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## Model_pojazdu_C4 Model_pojazdu_C4 Cactus Model_pojazdu_C4 Grand Picasso
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## Model_pojazdu_C4 Picasso Model_pojazdu_C5 Model_pojazdu_C5 Aircross
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## Model_pojazdu_C6 Model_pojazdu_C8 Model_pojazdu_Camry Model_pojazdu_Captiva
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## Model_pojazdu_Captur Model_pojazdu_Carens Model_pojazdu_Cayenne
## 1 0 0 0
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## Model_pojazdu_Ceed Model_pojazdu_Challenger Model_pojazdu_Charger
## 1 0 0 0
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## Model_pojazdu_Cherokee Model_pojazdu_City Model_pojazdu_Civic
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## Model_pojazdu_CL Model_pojazdu_CLA Model_pojazdu_Clio Model_pojazdu_CLS
## 1 0 0 0 0
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## Model_pojazdu_Clubman Model_pojazdu_Colt Model_pojazdu_Combo
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## Model_pojazdu_Compass Model_pojazdu_Cooper Model_pojazdu_Cooper S
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## Model_pojazdu_Corolla Model_pojazdu_Corsa Model_pojazdu_Countryman
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## Model_pojazdu_CR-V Model_pojazdu_Crafter Model_pojazdu_Crossland X
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## Model_pojazdu_Cruze Model_pojazdu_CTS Model_pojazdu_CX Model_pojazdu_CX-3
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## Model_pojazdu_CX-5 Model_pojazdu_CX-9 Model_pojazdu_Delta
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## Model_pojazdu_Discovery Model_pojazdu_Doblo Model_pojazdu_DS4
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## Model_pojazdu_DS5 Model_pojazdu_Duster Model_pojazdu_EcoSport
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## Model_pojazdu_EDGE Model_pojazdu_Elantra Model_pojazdu_EQC
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## Model_pojazdu_EQS Model_pojazdu_Escape Model_pojazdu_Espace
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## Model_pojazdu_Exeo Model_pojazdu_Expert Model_pojazdu_F150
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## Model_pojazdu_Fabia Model_pojazdu_Fiesta Model_pojazdu_Focus
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## Model_pojazdu_Focus C-Max Model_pojazdu_Forester Model_pojazdu_Forfour
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## Model_pojazdu_Freemont Model_pojazdu_Fusion Model_pojazdu_FX
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## Model_pojazdu_Galaxy Model_pojazdu_Garbus Model_pojazdu_Giulia
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## Model_pojazdu_Giulietta Model_pojazdu_GL Model_pojazdu_GLA
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## Model_pojazdu_GLC Model_pojazdu_GLE Model_pojazdu_GLK Model_pojazdu_Golf
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## Model_pojazdu_Golf Plus Model_pojazdu_Golf Sportsvan
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## Model_pojazdu_Grand Cherokee Model_pojazdu_Grand Espace
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## Model_pojazdu_Grand Scenic Model_pojazdu_Grand Vitara
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## Model_pojazdu_Grand Voyager Model_pojazdu_Grande Punto
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## Model_pojazdu_Grandis Model_pojazdu_Grandland Model_pojazdu_Grandland X
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## Model_pojazdu_HR-V Model_pojazdu_i10 Model_pojazdu_i20 Model_pojazdu_I30
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## Model_pojazdu_i30 N Model_pojazdu_i40 Model_pojazdu_Ibiza
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## Model_pojazdu_Impala Model_pojazdu_Inny Model_pojazdu_Insignia
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## Model_pojazdu_IS Model_pojazdu_ix35 Model_pojazdu_Jazz Model_pojazdu_Jetta
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## Model_pojazdu_Journey Model_pojazdu_Juke Model_pojazdu_Kadjar
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## Model_pojazdu_Kamiq Model_pojazdu_Kangoo Model_pojazdu_Klasa B
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## Model_pojazdu_Klasa C Model_pojazdu_Klasa E Model_pojazdu_Klasa S
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## Model_pojazdu_Kodiaq Model_pojazdu_Koleos Model_pojazdu_Kona
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## Model_pojazdu_Korando Model_pojazdu_Kuga Model_pojazdu_Laguna
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## Model_pojazdu_Lancer Model_pojazdu_Land Cruiser Model_pojazdu_Legacy
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## Model_pojazdu_Leon Model_pojazdu_Liana Model_pojazdu_Liberty
## 1 0 0 0
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## Model_pojazdu_Linea Model_pojazdu_Lodgy Model_pojazdu_Logan
## 1 0 0 0
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## Model_pojazdu_LX Model_pojazdu_Macan Model_pojazdu_Magentis
## 1 0 0 0
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## Model_pojazdu_Matrix Model_pojazdu_Megane Model_pojazdu_Meriva
## 1 0 0 0
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## Model_pojazdu_Micra Model_pojazdu_Mito Model_pojazdu_Modus
## 1 0 0 0
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## Model_pojazdu_Mokka Model_pojazdu_Mondeo Model_pojazdu_Murcielago
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## Model_pojazdu_Mustang Model_pojazdu_Navara Model_pojazdu_New Beetle
## 1 0 0 0
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## Model_pojazdu_Niro Model_pojazdu_Note Model_pojazdu_NX
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## Model_pojazdu_Octavia Model_pojazdu_ONE Model_pojazdu_Outback
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## Model_pojazdu_Outlander Model_pojazdu_Pacifica Model_pojazdu_Panda
## 1 0 0 0
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## Model_pojazdu_Partner Model_pojazdu_Passat Model_pojazdu_Patrol
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## Model_pojazdu_Picanto Model_pojazdu_Polo Model_pojazdu_Prius
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## Model_pojazdu_Prius+ Model_pojazdu_Pro_cee'd Model_pojazdu_ProAce
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## Model_pojazdu_Punto Model_pojazdu_Punto 2012 Model_pojazdu_Q3
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## Model_pojazdu_Q5 Model_pojazdu_Q7 Model_pojazdu_Q8 Model_pojazdu_Qashqai
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## Model_pojazdu_Qashqai+2 Model_pojazdu_Qubo Model_pojazdu_QX70
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## Model_pojazdu_Range Rover Sport Model_pojazdu_RAPID Model_pojazdu_RAV4
## 1 0 0 0
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## Model_pojazdu_Rifter Model_pojazdu_Rio Model_pojazdu_S-Max Model_pojazdu_S3
## 1 0 0 0 0
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## Model_pojazdu_S40 Model_pojazdu_S60 Model_pojazdu_S80 Model_pojazdu_Sandero
## 1 0 0 0 0
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## Model_pojazdu_Santa Fe Model_pojazdu_Scala Model_pojazdu_Scenic
## 1 0 0 0
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## Model_pojazdu_Scirocco Model_pojazdu_Scudo Model_pojazdu_Sedici
## 1 0 0 0
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## Model_pojazdu_Seria 1 Model_pojazdu_Seria 2 Model_pojazdu_Seria 3
## 1 0 0 0
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## 3 0 0 0
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## 247 0 0 0
## Model_pojazdu_Seria 4 Model_pojazdu_Seria 5 Model_pojazdu_Seria 6
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
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## Model_pojazdu_Seria 7 Model_pojazdu_Seria 8 Model_pojazdu_Seria 900
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## Model_pojazdu_Seria B Model_pojazdu_Sharan Model_pojazdu_Sierra
## 1 0 0 0
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## Model_pojazdu_Signum Model_pojazdu_Sirion Model_pojazdu_SL
## 1 0 0 0
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## Model_pojazdu_SLK Model_pojazdu_Sonata Model_pojazdu_Sorento
## 1 0 0 0
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## Model_pojazdu_Soul Model_pojazdu_Space Star Model_pojazdu_Sportage
## 1 0 0 0
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## Model_pojazdu_Stelvio Model_pojazdu_Stonic Model_pojazdu_Superb
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## Model_pojazdu_Swift Model_pojazdu_SX4 Model_pojazdu_SX4 S-Cross
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## Model_pojazdu_T-Roc Model_pojazdu_Talisman Model_pojazdu_Tercel
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## Model_pojazdu_Terios Model_pojazdu_Testarossa Model_pojazdu_Tigra
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## Model_pojazdu_Tiguan Model_pojazdu_Tipo Model_pojazdu_Toledo
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## Model_pojazdu_Tonale Model_pojazdu_Touareg Model_pojazdu_Touran
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## Model_pojazdu_Tourneo Courier Model_pojazdu_Town & Country
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## Model_pojazdu_Trafic Model_pojazdu_Transit Model_pojazdu_Transporter
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## Model_pojazdu_Trax Model_pojazdu_Tribeca Model_pojazdu_TT
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## 246 0 0 0
## 247 0 0 0
## Model_pojazdu_Tucson Model_pojazdu_V40 Model_pojazdu_V50 Model_pojazdu_V60
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
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## 14 0 0 0 0
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## 29 0 0 0 0
## 30 0 0 0 0
## 31 0 0 0 0
## 32 0 0 0 0
## 33 0 0 0 1
## 34 0 0 0 0
## 35 0 0 0 0
## 36 0 0 0 0
## 37 0 0 0 0
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## 147 0 0 0 0
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## 153 0 0 0 0
## 154 0 0 0 0
## 155 0 0 0 0
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## 162 0 0 0 0
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## 164 0 0 0 0
## 165 0 0 0 0
## 166 0 0 0 0
## 167 0 0 0 0
## 168 0 0 0 0
## 169 0 0 0 0
## 170 0 0 0 0
## 171 0 0 0 0
## 172 0 0 0 0
## 173 0 0 0 1
## 174 0 0 0 0
## 175 0 0 0 0
## 176 0 0 0 0
## 177 0 0 0 0
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## 184 0 0 0 0
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## 186 0 0 0 0
## 187 0 0 0 0
## 188 0 0 0 0
## 189 0 0 0 0
## 190 0 0 0 0
## 191 0 0 0 0
## 192 0 0 0 0
## 193 0 0 0 0
## 194 0 0 0 0
## 195 0 0 0 0
## 196 0 0 0 0
## 197 0 0 0 0
## 198 0 0 0 0
## 199 0 0 0 0
## 200 0 0 0 0
## 201 0 0 0 0
## 202 0 0 0 0
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## 205 0 0 1 0
## 206 0 0 0 0
## 207 0 0 0 0
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## 210 0 0 0 0
## 211 0 0 0 0
## 212 0 0 0 0
## 213 0 0 0 0
## 214 0 0 0 0
## 215 0 0 0 0
## 216 0 0 0 0
## 217 0 0 0 0
## 218 0 0 0 0
## 219 0 0 0 0
## 220 0 0 0 0
## 221 0 0 0 0
## 222 1 0 0 0
## 223 0 0 0 0
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## 225 0 0 0 0
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## 242 0 0 0 0
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## 244 0 0 0 0
## 245 0 0 0 0
## 246 0 0 0 0
## 247 0 0 0 0
## Model_pojazdu_V70 Model_pojazdu_V90 Cross Country Model_pojazdu_Vectra
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
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## 8 0 0 0
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## 188 0 0 0
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## 211 0 0 0
## 212 1 0 0
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## 221 0 0 0
## 222 0 0 0
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## 224 0 0 0
## 225 0 0 0
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## 233 0 0 0
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## 246 0 0 0
## 247 0 0 0
## Model_pojazdu_Venga Model_pojazdu_Verso Model_pojazdu_Vitara
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## 7 0 0 0
## 8 0 0 0
## 9 0 0 0
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## 189 0 0 0
## 190 0 0 0
## 191 0 0 0
## 192 0 0 0
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## 194 0 0 0
## 195 0 0 0
## 196 0 0 0
## 197 0 0 0
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## 199 0 0 0
## 200 0 0 0
## 201 0 0 0
## 202 0 0 0
## 203 0 0 1
## 204 0 0 0
## 205 0 0 0
## 206 0 0 0
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## 208 0 0 0
## 209 0 0 0
## 210 0 0 0
## 211 0 0 0
## 212 0 0 0
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## 217 0 0 0
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## 244 0 0 0
## 245 0 0 0
## 246 0 0 0
## 247 0 0 0
## Model_pojazdu_Vito Model_pojazdu_Vivaro Model_pojazdu_W124 (1984-1993)
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
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## 186 0 0 0
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## 190 0 0 0
## 191 0 0 0
## 192 0 0 0
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## 194 0 0 0
## 195 0 0 0
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## 199 0 0 0
## 200 0 0 0
## 201 0 0 0
## 202 0 0 0
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## 205 0 0 0
## 206 0 0 0
## 207 0 1 0
## 208 0 0 0
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## 210 0 0 0
## 211 0 0 0
## 212 0 0 0
## 213 0 0 0
## 214 0 0 0
## 215 0 0 0
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## 219 0 0 0
## 220 0 0 0
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## 244 0 0 0
## 245 0 0 0
## 246 0 0 0
## 247 0 0 0
## Model_pojazdu_Wrangler Model_pojazdu_X-Trail Model_pojazdu_X1
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
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## 18 0 0 0
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## Model_pojazdu_X2 Model_pojazdu_X3 Model_pojazdu_X4 Model_pojazdu_X5
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## Model_pojazdu_X5 M Model_pojazdu_X6 Model_pojazdu_XC 40 Model_pojazdu_XC 60
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## Model_pojazdu_XC 70 Model_pojazdu_XC 90 Model_pojazdu_XCeed
## 1 0 0 0
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## Model_pojazdu_XE Model_pojazdu_XF Model_pojazdu_XL7
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## 128 0 0 0
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## 143 0 0 0
## 144 0 0 0
## 145 0 0 0
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## 152 0 0 0
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## 158 0 0 0
## 159 0 0 0
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## 161 0 0 0
## 162 0 0 0
## 163 0 0 0
## 164 0 0 0
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## 169 0 0 0
## 170 0 0 0
## 171 0 0 0
## 172 0 0 0
## 173 0 0 0
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## 175 0 0 0
## 176 0 0 0
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## 178 0 0 0
## 179 0 0 0
## 180 0 0 0
## 181 0 0 0
## 182 0 0 0
## 183 0 0 0
## 184 0 0 0
## 185 0 0 0
## 186 0 0 0
## 187 0 0 0
## 188 0 0 0
## 189 0 0 0
## 190 0 0 0
## 191 0 0 0
## 192 0 0 0
## 193 0 0 0
## 194 0 0 0
## 195 0 0 0
## 196 0 0 0
## 197 0 0 0
## 198 0 0 0
## 199 0 0 0
## 200 0 0 0
## 201 0 0 0
## 202 0 0 0
## 203 0 0 0
## 204 0 0 0
## 205 0 0 0
## 206 0 0 0
## 207 0 0 0
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## 209 0 0 0
## 210 0 0 0
## 211 0 0 0
## 212 0 0 0
## 213 0 0 0
## 214 0 0 0
## 215 0 0 0
## 216 0 0 0
## 217 0 0 0
## 218 0 0 0
## 219 0 0 0
## 220 0 0 0
## 221 0 0 0
## 222 0 0 0
## 223 0 0 0
## 224 0 0 0
## 225 0 0 0
## 226 0 0 0
## 227 0 0 0
## 228 0 0 0
## 229 0 0 0
## 230 0 0 0
## 231 0 0 0
## 232 0 0 0
## 233 0 0 0
## 234 0 0 0
## 235 0 0 0
## 236 0 0 0
## 237 0 0 0
## 238 0 0 0
## 239 0 0 0
## 240 0 0 0
## 241 0 0 0
## 242 0 0 0
## 243 0 0 0
## 244 0 0 0
## 245 0 0 0
## 246 0 0 0
## 247 0 0 0
## Model_pojazdu_Xsara Picasso Model_pojazdu_Yaris Model_pojazdu_Zafira
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## 7 0 0 0
## 8 0 0 0
## 9 0 0 0
## 10 0 0 0
## 11 0 0 0
## 12 0 0 0
## 13 0 0 0
## 14 0 0 0
## 15 0 0 0
## 16 0 0 0
## 17 0 0 0
## 18 0 0 0
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## 20 0 0 0
## 21 0 0 0
## 22 0 0 0
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## 115 0 0 0
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## 120 0 0 0
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## 123 0 0 0
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## 128 0 0 0
## 129 0 0 0
## 130 0 0 0
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## 132 0 0 0
## 133 0 0 0
## 134 0 0 0
## 135 0 0 0
## 136 0 0 0
## 137 0 0 0
## 138 0 0 0
## 139 0 0 0
## 140 0 0 0
## 141 0 0 0
## 142 0 0 0
## 143 0 0 0
## 144 0 0 0
## 145 0 0 0
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## 147 0 0 0
## 148 0 0 0
## 149 0 0 0
## 150 0 0 0
## 151 0 0 0
## 152 0 0 0
## 153 0 0 0
## 154 0 0 0
## 155 0 0 0
## 156 0 0 0
## 157 0 0 0
## 158 0 0 0
## 159 0 0 0
## 160 0 0 0
## 161 0 0 0
## 162 0 0 0
## 163 0 0 0
## 164 0 0 0
## 165 0 0 0
## 166 0 0 0
## 167 0 0 0
## 168 0 0 0
## 169 0 0 0
## 170 0 0 0
## 171 0 0 0
## 172 0 0 0
## 173 0 0 0
## 174 0 0 0
## 175 0 0 0
## 176 0 0 0
## 177 0 0 0
## 178 0 0 0
## 179 0 0 0
## 180 0 1 0
## 181 0 0 0
## 182 0 0 0
## 183 0 0 0
## 184 0 0 0
## 185 0 0 0
## 186 0 0 0
## 187 0 0 0
## 188 0 0 0
## 189 0 0 0
## 190 0 1 0
## 191 0 0 0
## 192 0 0 0
## 193 0 0 0
## 194 0 0 0
## 195 0 0 0
## 196 0 1 0
## 197 0 0 0
## 198 0 0 0
## 199 0 0 0
## 200 0 0 0
## 201 0 0 0
## 202 0 0 0
## 203 0 0 0
## 204 0 0 0
## 205 0 0 0
## 206 0 0 0
## 207 0 0 0
## 208 0 0 0
## 209 0 0 0
## 210 0 0 0
## 211 0 0 0
## 212 0 0 0
## 213 0 0 0
## 214 0 0 0
## 215 0 0 0
## 216 0 0 0
## 217 0 0 0
## 218 0 0 0
## 219 0 0 0
## 220 0 0 0
## 221 0 0 0
## 222 0 0 0
## 223 0 0 0
## 224 0 0 0
## 225 0 0 0
## 226 0 0 0
## 227 0 0 0
## 228 0 0 0
## 229 0 0 0
## 230 0 0 0
## 231 0 0 0
## 232 0 0 0
## 233 0 0 0
## 234 0 0 0
## 235 0 0 0
## 236 0 0 0
## 237 0 0 0
## 238 0 0 0
## 239 0 0 0
## 240 0 0 0
## 241 0 0 0
## 242 0 0 0
## 243 0 0 0
## 244 0 0 0
## 245 0 0 0
## 246 0 0 0
## 247 0 0 0
## Model_pojazdu_Zoe Rodzaj_paliwa_Benzyna Rodzaj_paliwa_Benzyna+LPG
## 1 0 1 0
## 2 0 0 0
## 3 0 0 0
## 4 0 1 0
## 5 0 0 0
## 6 0 0 0
## 7 0 1 0
## 8 0 0 0
## 9 0 0 0
## 10 0 1 0
## 11 0 0 0
## 12 0 1 0
## 13 0 1 0
## 14 0 0 0
## 15 0 0 1
## 16 0 1 0
## 17 0 0 0
## 18 0 1 0
## 19 0 1 0
## 20 0 0 0
## 21 0 1 0
## 22 0 1 0
## 23 0 0 0
## 24 0 1 0
## 25 0 1 0
## 26 0 0 0
## 27 0 1 0
## 28 0 1 0
## 29 0 0 1
## 30 0 1 0
## 31 0 1 0
## 32 0 0 1
## 33 0 0 0
## 34 0 1 0
## 35 0 1 0
## 36 0 0 0
## 37 0 1 0
## 38 0 1 0
## 39 0 1 0
## 40 0 0 1
## 41 0 0 0
## 42 0 1 0
## 43 0 0 0
## 44 0 0 1
## 45 0 1 0
## 46 0 1 0
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## 48 0 0 0
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## 50 0 0 0
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## 57 0 0 0
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## 73 0 0 1
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## 84 0 1 0
## 85 0 0 0
## 86 0 0 1
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## 91 0 0 1
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## 93 0 1 0
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## 95 0 1 0
## 96 0 0 0
## 97 0 1 0
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## 100 0 0 0
## 101 0 1 0
## 102 0 1 0
## 103 0 0 0
## 104 0 1 0
## 105 0 0 0
## 106 0 0 0
## 107 0 0 0
## 108 0 0 0
## 109 0 0 1
## 110 0 1 0
## 111 0 0 0
## 112 0 0 0
## 113 0 1 0
## 114 0 0 0
## 115 0 1 0
## 116 0 0 1
## 117 0 0 0
## 118 0 1 0
## 119 0 0 0
## 120 0 0 0
## 121 0 1 0
## 122 0 1 0
## 123 0 0 0
## 124 0 1 0
## 125 0 1 0
## 126 0 0 0
## 127 0 1 0
## 128 0 0 0
## 129 0 0 0
## 130 0 0 0
## 131 0 0 0
## 132 0 1 0
## 133 0 0 0
## 134 0 0 1
## 135 0 1 0
## 136 0 1 0
## 137 0 1 0
## 138 0 0 0
## 139 0 0 1
## 140 0 0 0
## 141 0 1 0
## 142 0 1 0
## 143 0 0 0
## 144 0 1 0
## 145 0 1 0
## 146 0 0 1
## 147 0 1 0
## 148 0 0 0
## 149 0 1 0
## 150 0 0 0
## 151 0 1 0
## 152 0 1 0
## 153 0 0 0
## 154 0 0 0
## 155 0 1 0
## 156 0 1 0
## 157 0 0 0
## 158 0 1 0
## 159 0 1 0
## 160 0 1 0
## 161 0 1 0
## 162 0 0 0
## 163 0 0 1
## 164 0 0 0
## 165 0 1 0
## 166 0 1 0
## 167 0 1 0
## 168 0 0 0
## 169 0 1 0
## 170 0 0 0
## 171 0 0 0
## 172 0 1 0
## 173 0 0 0
## 174 0 0 0
## 175 0 1 0
## 176 0 0 0
## 177 0 1 0
## 178 0 0 0
## 179 0 1 0
## 180 0 1 0
## 181 0 0 0
## 182 0 0 0
## 183 0 0 0
## 184 0 0 0
## 185 0 1 0
## 186 0 0 1
## 187 0 1 0
## 188 0 0 0
## 189 0 1 0
## 190 0 1 0
## 191 0 0 0
## 192 0 0 0
## 193 0 0 0
## 194 0 1 0
## 195 0 0 0
## 196 0 1 0
## 197 0 0 0
## 198 0 0 0
## 199 0 0 1
## 200 0 1 0
## 201 0 1 0
## 202 0 1 0
## 203 0 0 0
## 204 0 0 0
## 205 0 1 0
## 206 0 0 0
## 207 0 0 0
## 208 0 0 0
## 209 0 1 0
## 210 0 0 0
## 211 0 1 0
## 212 0 0 0
## 213 0 1 0
## 214 0 1 0
## 215 0 0 1
## 216 0 0 0
## 217 0 0 0
## 218 0 1 0
## 219 0 0 0
## 220 0 1 0
## 221 0 0 0
## 222 0 0 0
## 223 0 0 0
## 224 0 1 0
## 225 0 1 0
## 226 0 0 0
## 227 0 0 0
## 228 0 0 0
## 229 0 0 0
## 230 0 1 0
## 231 0 0 1
## 232 0 0 0
## 233 0 0 1
## 234 0 0 0
## 235 0 1 0
## 236 0 0 0
## 237 0 0 0
## 238 0 1 0
## 239 0 0 0
## 240 0 1 0
## 241 0 1 0
## 242 0 1 0
## 243 0 0 0
## 244 0 0 0
## 245 0 0 0
## 246 0 0 0
## 247 0 1 0
## Rodzaj_paliwa_Diesel Rodzaj_paliwa_Elektryczny Rodzaj_paliwa_Hybryda
## 1 0 0 0
## 2 1 0 0
## 3 1 0 0
## 4 0 0 0
## 5 0 0 1
## 6 1 0 0
## 7 0 0 0
## 8 1 0 0
## 9 1 0 0
## 10 0 0 0
## 11 1 0 0
## 12 0 0 0
## 13 0 0 0
## 14 1 0 0
## 15 0 0 0
## 16 0 0 0
## 17 1 0 0
## 18 0 0 0
## 19 0 0 0
## 20 1 0 0
## 21 0 0 0
## 22 0 0 0
## 23 0 0 1
## 24 0 0 0
## 25 0 0 0
## 26 1 0 0
## 27 0 0 0
## 28 0 0 0
## 29 0 0 0
## 30 0 0 0
## 31 0 0 0
## 32 0 0 0
## 33 1 0 0
## 34 0 0 0
## 35 0 0 0
## 36 0 0 1
## 37 0 0 0
## 38 0 0 0
## 39 0 0 0
## 40 0 0 0
## 41 1 0 0
## 42 0 0 0
## 43 1 0 0
## 44 0 0 0
## 45 0 0 0
## 46 0 0 0
## 47 1 0 0
## 48 0 0 1
## 49 1 0 0
## 50 1 0 0
## 51 1 0 0
## 52 1 0 0
## 53 1 0 0
## 54 0 0 0
## 55 1 0 0
## 56 0 0 0
## 57 1 0 0
## 58 0 0 0
## 59 0 0 0
## 60 0 0 0
## 61 0 0 0
## 62 0 0 0
## 63 0 0 0
## 64 1 0 0
## 65 1 0 0
## 66 0 0 0
## 67 0 0 0
## 68 0 0 0
## 69 1 0 0
## 70 0 0 0
## 71 1 0 0
## 72 1 0 0
## 73 0 0 0
## 74 0 0 1
## 75 1 0 0
## 76 0 0 0
## 77 0 0 0
## 78 0 0 0
## 79 1 0 0
## 80 0 0 0
## 81 1 0 0
## 82 0 0 0
## 83 1 0 0
## 84 0 0 0
## 85 1 0 0
## 86 0 0 0
## 87 1 0 0
## 88 1 0 0
## 89 1 0 0
## 90 1 0 0
## 91 0 0 0
## 92 0 0 0
## 93 0 0 0
## 94 1 0 0
## 95 0 0 0
## 96 1 0 0
## 97 0 0 0
## 98 0 0 0
## 99 0 0 0
## 100 1 0 0
## 101 0 0 0
## 102 0 0 0
## 103 1 0 0
## 104 0 0 0
## 105 1 0 0
## 106 1 0 0
## 107 1 0 0
## 108 1 0 0
## 109 0 0 0
## 110 0 0 0
## 111 1 0 0
## 112 1 0 0
## 113 0 0 0
## 114 1 0 0
## 115 0 0 0
## 116 0 0 0
## 117 1 0 0
## 118 0 0 0
## 119 1 0 0
## 120 1 0 0
## 121 0 0 0
## 122 0 0 0
## 123 1 0 0
## 124 0 0 0
## 125 0 0 0
## 126 1 0 0
## 127 0 0 0
## 128 1 0 0
## 129 1 0 0
## 130 1 0 0
## 131 1 0 0
## 132 0 0 0
## 133 1 0 0
## 134 0 0 0
## 135 0 0 0
## 136 0 0 0
## 137 0 0 0
## 138 1 0 0
## 139 0 0 0
## 140 1 0 0
## 141 0 0 0
## 142 0 0 0
## 143 1 0 0
## 144 0 0 0
## 145 0 0 0
## 146 0 0 0
## 147 0 0 0
## 148 1 0 0
## 149 0 0 0
## 150 1 0 0
## 151 0 0 0
## 152 0 0 0
## 153 1 0 0
## 154 1 0 0
## 155 0 0 0
## 156 0 0 0
## 157 0 0 1
## 158 0 0 0
## 159 0 0 0
## 160 0 0 0
## 161 0 0 0
## 162 1 0 0
## 163 0 0 0
## 164 0 0 1
## 165 0 0 0
## 166 0 0 0
## 167 0 0 0
## 168 1 0 0
## 169 0 0 0
## 170 1 0 0
## 171 1 0 0
## 172 0 0 0
## 173 1 0 0
## 174 0 0 1
## 175 0 0 0
## 176 1 0 0
## 177 0 0 0
## 178 1 0 0
## 179 0 0 0
## 180 0 0 0
## 181 0 0 1
## 182 0 0 1
## 183 1 0 0
## 184 1 0 0
## 185 0 0 0
## 186 0 0 0
## 187 0 0 0
## 188 1 0 0
## 189 0 0 0
## 190 0 0 0
## 191 1 0 0
## 192 1 0 0
## 193 1 0 0
## 194 0 0 0
## 195 1 0 0
## 196 0 0 0
## 197 1 0 0
## 198 1 0 0
## 199 0 0 0
## 200 0 0 0
## 201 0 0 0
## 202 0 0 0
## 203 0 0 1
## 204 1 0 0
## 205 0 0 0
## 206 1 0 0
## 207 1 0 0
## 208 1 0 0
## 209 0 0 0
## 210 1 0 0
## 211 0 0 0
## 212 1 0 0
## 213 0 0 0
## 214 0 0 0
## 215 0 0 0
## 216 0 0 1
## 217 1 0 0
## 218 0 0 0
## 219 1 0 0
## 220 0 0 0
## 221 1 0 0
## 222 1 0 0
## 223 1 0 0
## 224 0 0 0
## 225 0 0 0
## 226 1 0 0
## 227 1 0 0
## 228 1 0 0
## 229 1 0 0
## 230 0 0 0
## 231 0 0 0
## 232 1 0 0
## 233 0 0 0
## 234 1 0 0
## 235 0 0 0
## 236 1 0 0
## 237 1 0 0
## 238 0 0 0
## 239 1 0 0
## 240 0 0 0
## 241 0 0 0
## 242 0 0 0
## 243 1 0 0
## 244 1 0 0
## 245 1 0 0
## 246 1 0 0
## 247 0 0 0
## Typ_nadwozia_Auta małe Typ_nadwozia_Auta miejskie Typ_nadwozia_Coupe
## 1 0 1 0
## 2 0 0 0
## 3 0 0 0
## 4 0 1 0
## 5 0 0 0
## 6 0 0 0
## 7 0 0 0
## 8 0 0 0
## 9 0 0 0
## 10 0 0 0
## 11 0 0 0
## 12 0 0 0
## 13 0 1 0
## 14 0 0 0
## 15 0 0 0
## 16 0 0 0
## 17 0 0 0
## 18 0 0 0
## 19 0 0 0
## 20 0 0 0
## 21 0 0 0
## 22 0 1 0
## 23 1 0 0
## 24 0 1 0
## 25 0 0 0
## 26 0 0 0
## 27 0 0 0
## 28 0 0 0
## 29 0 0 0
## 30 0 1 0
## 31 0 1 0
## 32 0 0 0
## 33 0 0 0
## 34 0 0 0
## 35 0 0 0
## 36 0 0 0
## 37 0 0 0
## 38 0 0 0
## 39 0 0 0
## 40 0 0 0
## 41 0 0 0
## 42 0 0 0
## 43 0 0 0
## 44 0 1 0
## 45 0 0 0
## 46 0 0 0
## 47 0 0 0
## 48 0 0 0
## 49 0 0 0
## 50 0 0 0
## 51 0 1 0
## 52 0 0 0
## 53 0 0 0
## 54 0 0 0
## 55 0 0 0
## 56 0 0 0
## 57 0 0 0
## 58 0 0 0
## 59 0 0 0
## 60 0 0 0
## 61 0 0 0
## 62 0 0 0
## 63 0 0 0
## 64 0 0 0
## 65 0 0 0
## 66 0 0 0
## 67 0 1 0
## 68 0 0 0
## 69 0 0 0
## 70 0 0 0
## 71 0 0 0
## 72 0 0 0
## 73 0 0 0
## 74 0 0 0
## 75 0 0 0
## 76 0 0 0
## 77 0 1 0
## 78 0 0 1
## 79 0 0 0
## 80 0 0 0
## 81 0 0 0
## 82 0 0 0
## 83 0 0 0
## 84 0 0 0
## 85 0 0 0
## 86 0 0 0
## 87 0 0 0
## 88 0 0 0
## 89 0 0 0
## 90 0 0 0
## 91 0 0 0
## 92 1 0 0
## 93 0 0 0
## 94 0 0 0
## 95 0 0 0
## 96 0 0 0
## 97 0 0 0
## 98 0 0 0
## 99 0 0 0
## 100 0 0 0
## 101 0 0 0
## 102 0 0 0
## 103 0 0 0
## 104 0 0 0
## 105 0 0 0
## 106 0 0 0
## 107 0 0 0
## 108 0 0 0
## 109 0 0 0
## 110 0 0 0
## 111 0 0 0
## 112 0 0 0
## 113 0 1 0
## 114 0 0 0
## 115 0 1 0
## 116 0 0 0
## 117 0 0 0
## 118 0 0 0
## 119 0 0 0
## 120 0 0 0
## 121 0 0 0
## 122 0 0 0
## 123 0 0 0
## 124 0 1 0
## 125 0 0 1
## 126 0 0 0
## 127 0 0 0
## 128 0 0 0
## 129 0 0 0
## 130 0 0 0
## 131 0 0 0
## 132 0 1 0
## 133 0 0 0
## 134 0 0 0
## 135 0 0 0
## 136 0 0 0
## 137 0 0 0
## 138 0 0 0
## 139 0 0 0
## 140 0 0 0
## 141 1 0 0
## 142 0 0 0
## 143 0 0 0
## 144 0 1 0
## 145 0 0 0
## 146 0 0 0
## 147 0 0 0
## 148 0 0 0
## 149 0 0 0
## 150 0 0 0
## 151 0 0 0
## 152 1 0 0
## 153 0 0 0
## 154 0 0 1
## 155 0 0 1
## 156 0 0 0
## 157 0 0 0
## 158 0 0 0
## 159 0 0 0
## 160 0 0 0
## 161 0 0 1
## 162 0 0 0
## 163 0 0 0
## 164 0 0 0
## 165 0 0 0
## 166 0 0 0
## 167 0 0 0
## 168 0 0 0
## 169 0 1 0
## 170 0 0 0
## 171 0 1 0
## 172 0 1 0
## 173 0 0 0
## 174 0 0 0
## 175 0 0 0
## 176 0 0 0
## 177 0 0 0
## 178 1 0 0
## 179 1 0 0
## 180 0 0 0
## 181 0 0 0
## 182 0 0 0
## 183 0 0 0
## 184 0 0 0
## 185 0 0 0
## 186 0 0 0
## 187 0 0 0
## 188 0 0 0
## 189 0 0 1
## 190 0 1 0
## 191 0 0 0
## 192 0 0 0
## 193 0 0 0
## 194 0 0 0
## 195 0 0 0
## 196 0 1 0
## 197 0 0 0
## 198 0 0 0
## 199 0 0 0
## 200 0 0 0
## 201 0 0 0
## 202 0 0 0
## 203 0 0 0
## 204 0 0 0
## 205 0 0 0
## 206 1 0 0
## 207 0 0 0
## 208 0 0 0
## 209 0 0 0
## 210 0 0 0
## 211 0 1 0
## 212 0 0 0
## 213 0 1 0
## 214 0 0 0
## 215 0 0 0
## 216 0 0 0
## 217 0 0 0
## 218 0 0 0
## 219 0 0 0
## 220 0 0 0
## 221 0 0 0
## 222 0 0 0
## 223 0 0 0
## 224 0 0 0
## 225 0 0 1
## 226 0 0 0
## 227 0 0 0
## 228 0 0 0
## 229 0 0 0
## 230 0 0 0
## 231 0 0 0
## 232 0 0 0
## 233 0 0 0
## 234 0 0 0
## 235 1 0 0
## 236 0 0 0
## 237 0 0 0
## 238 0 0 0
## 239 0 0 0
## 240 0 0 0
## 241 0 0 0
## 242 0 0 0
## 243 0 0 0
## 244 0 0 0
## 245 0 0 0
## 246 0 0 0
## 247 0 0 0
## Typ_nadwozia_Kabriolet Typ_nadwozia_Kombi Typ_nadwozia_Kompakt
## 1 0 0 0
## 2 0 1 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 1
## 6 0 0 0
## 7 0 0 0
## 8 0 0 0
## 9 0 1 0
## 10 0 0 0
## 11 0 1 0
## 12 0 0 1
## 13 0 0 0
## 14 0 0 0
## 15 0 0 0
## 16 0 0 1
## 17 0 1 0
## 18 0 0 0
## 19 0 0 1
## 20 0 0 0
## 21 0 0 0
## 22 0 0 0
## 23 0 0 0
## 24 0 0 0
## 25 0 1 0
## 26 0 0 0
## 27 0 1 0
## 28 0 0 0
## 29 0 0 0
## 30 0 0 0
## 31 0 0 0
## 32 0 0 1
## 33 0 1 0
## 34 0 0 0
## 35 0 0 0
## 36 0 0 0
## 37 0 0 1
## 38 0 1 0
## 39 0 0 0
## 40 0 0 0
## 41 0 1 0
## 42 0 0 0
## 43 0 1 0
## 44 0 0 0
## 45 0 0 0
## 46 0 0 0
## 47 0 0 0
## 48 0 0 0
## 49 0 1 0
## 50 0 0 0
## 51 0 0 0
## 52 0 1 0
## 53 0 0 1
## 54 0 0 0
## 55 0 0 1
## 56 0 0 0
## 57 0 0 0
## 58 0 0 0
## 59 0 0 0
## 60 0 0 1
## 61 0 0 1
## 62 0 0 0
## 63 0 0 1
## 64 0 0 0
## 65 0 0 0
## 66 0 1 0
## 67 0 0 0
## 68 0 0 0
## 69 0 1 0
## 70 0 0 0
## 71 0 0 0
## 72 0 0 0
## 73 0 0 0
## 74 0 0 0
## 75 0 0 0
## 76 0 0 0
## 77 0 0 0
## 78 0 0 0
## 79 0 0 0
## 80 0 0 0
## 81 0 0 0
## 82 0 0 0
## 83 0 0 1
## 84 0 0 1
## 85 0 0 0
## 86 0 0 0
## 87 0 0 1
## 88 0 0 0
## 89 0 0 1
## 90 0 1 0
## 91 0 1 0
## 92 0 0 0
## 93 0 0 0
## 94 0 1 0
## 95 0 0 0
## 96 0 0 0
## 97 0 1 0
## 98 0 0 1
## 99 0 0 0
## 100 0 1 0
## 101 0 0 1
## 102 0 0 0
## 103 0 0 1
## 104 0 0 0
## 105 0 1 0
## 106 0 0 0
## 107 0 0 0
## 108 0 0 0
## 109 0 0 1
## 110 0 1 0
## 111 0 0 0
## 112 0 0 0
## 113 0 0 0
## 114 0 0 0
## 115 0 0 0
## 116 0 0 0
## 117 0 0 0
## 118 0 0 1
## 119 0 1 0
## 120 0 0 0
## 121 0 0 0
## 122 0 0 0
## 123 0 0 0
## 124 0 0 0
## 125 0 0 0
## 126 0 1 0
## 127 0 0 1
## 128 0 0 0
## 129 0 1 0
## 130 0 0 0
## 131 0 1 0
## 132 0 0 0
## 133 0 0 0
## 134 0 1 0
## 135 1 0 0
## 136 0 0 1
## 137 0 0 1
## 138 0 0 1
## 139 0 0 0
## 140 0 0 0
## 141 0 0 0
## 142 0 1 0
## 143 0 1 0
## 144 0 0 0
## 145 0 0 0
## 146 0 0 1
## 147 0 0 0
## 148 0 0 0
## 149 0 0 0
## 150 0 1 0
## 151 0 0 0
## 152 0 0 0
## 153 0 0 1
## 154 0 0 0
## 155 0 0 0
## 156 0 0 0
## 157 0 0 0
## 158 1 0 0
## 159 0 0 1
## 160 0 0 0
## 161 0 0 0
## 162 0 0 0
## 163 0 1 0
## 164 0 0 1
## 165 0 0 0
## 166 0 0 0
## 167 0 1 0
## 168 0 0 0
## 169 0 0 0
## 170 0 0 1
## 171 0 0 0
## 172 0 0 0
## 173 0 1 0
## 174 0 0 0
## 175 0 1 0
## 176 0 1 0
## 177 0 0 1
## 178 0 0 0
## 179 0 0 0
## 180 0 0 1
## 181 0 0 0
## 182 0 1 0
## 183 0 0 0
## 184 0 0 0
## 185 0 0 0
## 186 0 0 0
## 187 0 0 1
## 188 1 0 0
## 189 0 0 0
## 190 0 0 0
## 191 0 0 0
## 192 0 0 0
## 193 0 0 0
## 194 0 1 0
## 195 0 1 0
## 196 0 0 0
## 197 0 0 0
## 198 0 1 0
## 199 0 1 0
## 200 0 1 0
## 201 0 0 1
## 202 0 0 0
## 203 0 0 0
## 204 0 0 0
## 205 0 1 0
## 206 0 0 0
## 207 0 0 0
## 208 0 0 0
## 209 0 0 1
## 210 0 0 0
## 211 0 0 0
## 212 0 1 0
## 213 0 0 0
## 214 0 0 0
## 215 0 0 0
## 216 0 0 0
## 217 0 0 1
## 218 0 1 0
## 219 0 1 0
## 220 0 1 0
## 221 0 0 1
## 222 0 0 0
## 223 0 0 0
## 224 0 0 0
## 225 0 0 0
## 226 0 0 1
## 227 0 0 0
## 228 0 0 0
## 229 0 0 0
## 230 0 0 1
## 231 0 0 1
## 232 0 0 0
## 233 0 0 1
## 234 0 0 1
## 235 0 0 0
## 236 0 0 0
## 237 0 1 0
## 238 0 1 0
## 239 0 0 0
## 240 0 0 1
## 241 0 0 1
## 242 0 0 0
## 243 0 0 0
## 244 0 1 0
## 245 0 1 0
## 246 0 0 0
## 247 0 1 0
## Typ_nadwozia_Minivan Typ_nadwozia_Sedan Typ_nadwozia_SUV Kolor_Beżowy
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 1 1
## 4 0 0 0 0
## 5 0 0 0 0
## 6 1 0 0 0
## 7 0 0 1 0
## 8 0 1 0 0
## 9 0 0 0 0
## 10 0 0 1 0
## 11 0 0 0 0
## 12 0 0 0 0
## 13 0 0 0 1
## 14 0 1 0 0
## 15 0 1 0 0
## 16 0 0 0 0
## 17 0 0 0 0
## 18 0 0 1 0
## 19 0 0 0 1
## 20 0 0 1 0
## 21 0 0 1 0
## 22 0 0 0 0
## 23 0 0 0 0
## 24 0 0 0 0
## 25 0 0 0 0
## 26 1 0 0 0
## 27 0 0 0 0
## 28 0 0 1 0
## 29 0 1 0 0
## 30 0 0 0 0
## 31 0 0 0 0
## 32 0 0 0 0
## 33 0 0 0 0
## 34 1 0 0 0
## 35 1 0 0 0
## 36 0 0 1 0
## 37 0 0 0 0
## 38 0 0 0 0
## 39 0 0 1 0
## 40 1 0 0 0
## 41 0 0 0 0
## 42 0 1 0 0
## 43 0 0 0 0
## 44 0 0 0 0
## 45 0 1 0 0
## 46 0 0 1 0
## 47 1 0 0 0
## 48 0 0 1 0
## 49 0 0 0 0
## 50 1 0 0 0
## 51 0 0 0 0
## 52 0 0 0 0
## 53 0 0 0 0
## 54 0 1 0 0
## 55 0 0 0 0
## 56 0 1 0 0
## 57 0 0 1 0
## 58 0 0 1 0
## 59 0 1 0 0
## 60 0 0 0 0
## 61 0 0 0 0
## 62 0 0 1 0
## 63 0 0 0 0
## 64 1 0 0 0
## 65 1 0 0 0
## 66 0 0 0 0
## 67 0 0 0 0
## 68 0 1 0 0
## 69 0 0 0 0
## 70 0 1 0 0
## 71 1 0 0 0
## 72 0 0 1 0
## 73 0 1 0 0
## 74 0 0 1 0
## 75 0 1 0 0
## 76 0 1 0 0
## 77 0 0 0 0
## 78 0 0 0 0
## 79 0 0 1 0
## 80 0 1 0 0
## 81 0 1 0 0
## 82 0 1 0 0
## 83 0 0 0 0
## 84 0 0 0 0
## 85 0 0 1 0
## 86 0 1 0 0
## 87 0 0 0 0
## 88 0 0 1 0
## 89 0 0 0 0
## 90 0 0 0 0
## 91 0 0 0 0
## 92 0 0 0 0
## 93 0 0 1 0
## 94 0 0 0 0
## 95 0 1 0 0
## 96 0 0 1 0
## 97 0 0 0 0
## 98 0 0 0 0
## 99 0 0 1 0
## 100 0 0 0 1
## 101 0 0 0 0
## 102 0 0 1 0
## 103 0 0 0 0
## 104 0 1 0 0
## 105 0 0 0 0
## 106 0 0 1 0
## 107 1 0 0 0
## 108 0 0 1 0
## 109 0 0 0 0
## 110 0 0 0 0
## 111 0 0 1 0
## 112 0 1 0 0
## 113 0 0 0 0
## 114 1 0 0 0
## 115 0 0 0 0
## 116 1 0 0 0
## 117 0 0 1 0
## 118 0 0 0 0
## 119 0 0 0 0
## 120 0 0 1 0
## 121 0 1 0 0
## 122 0 1 0 0
## 123 1 0 0 0
## 124 0 0 0 0
## 125 0 0 0 0
## 126 0 0 0 0
## 127 0 0 0 0
## 128 0 0 1 0
## 129 0 0 0 0
## 130 0 0 1 0
## 131 0 0 0 0
## 132 0 0 0 0
## 133 0 0 1 0
## 134 0 0 0 0
## 135 0 0 0 0
## 136 0 0 0 0
## 137 0 0 0 0
## 138 0 0 0 0
## 139 0 0 1 0
## 140 0 1 0 0
## 141 0 0 0 0
## 142 0 0 0 0
## 143 0 0 0 0
## 144 0 0 0 0
## 145 0 0 1 0
## 146 0 0 0 0
## 147 0 1 0 0
## 148 1 0 0 0
## 149 0 0 1 0
## 150 0 0 0 0
## 151 0 1 0 0
## 152 0 0 0 0
## 153 0 0 0 0
## 154 0 0 0 0
## 155 0 0 0 0
## 156 0 1 0 0
## 157 0 0 1 0
## 158 0 0 0 0
## 159 0 0 0 0
## 160 0 0 1 0
## 161 0 0 0 0
## 162 0 1 0 0
## 163 0 0 0 0
## 164 0 0 0 0
## 165 0 0 1 0
## 166 0 1 0 0
## 167 0 0 0 0
## 168 0 1 0 0
## 169 0 0 0 0
## 170 0 0 0 0
## 171 0 0 0 0
## 172 0 0 0 0
## 173 0 0 0 0
## 174 1 0 0 0
## 175 0 0 0 0
## 176 0 0 0 0
## 177 0 0 0 0
## 178 0 0 0 0
## 179 0 0 0 0
## 180 0 0 0 0
## 181 0 0 1 0
## 182 0 0 0 0
## 183 0 1 0 0
## 184 1 0 0 0
## 185 0 0 1 0
## 186 0 0 1 0
## 187 0 0 0 0
## 188 0 0 0 0
## 189 0 0 0 0
## 190 0 0 0 0
## 191 0 1 0 0
## 192 0 1 0 0
## 193 0 0 1 0
## 194 0 0 0 0
## 195 0 0 0 0
## 196 0 0 0 0
## 197 0 0 1 0
## 198 0 0 0 0
## 199 0 0 0 0
## 200 0 0 0 0
## 201 0 0 0 0
## 202 0 1 0 0
## 203 0 0 1 1
## 204 0 1 0 0
## 205 0 0 0 0
## 206 0 0 0 0
## 207 1 0 0 0
## 208 0 0 1 0
## 209 0 0 0 0
## 210 0 1 0 0
## 211 0 0 0 0
## 212 0 0 0 0
## 213 0 0 0 0
## 214 0 1 0 0
## 215 0 0 1 0
## 216 0 0 1 0
## 217 0 0 0 0
## 218 0 0 0 0
## 219 0 0 0 0
## 220 0 0 0 0
## 221 0 0 0 0
## 222 0 0 1 0
## 223 0 0 1 0
## 224 0 0 1 0
## 225 0 0 0 0
## 226 0 0 0 0
## 227 1 0 0 0
## 228 0 0 1 0
## 229 0 1 0 0
## 230 0 0 0 0
## 231 0 0 0 0
## 232 1 0 0 0
## 233 0 0 0 0
## 234 0 0 0 0
## 235 0 0 0 0
## 236 0 0 1 0
## 237 0 0 0 0
## 238 0 0 0 0
## 239 0 0 1 0
## 240 0 0 0 0
## 241 0 0 0 0
## 242 1 0 0 0
## 243 0 0 1 0
## 244 0 0 0 0
## 245 0 0 0 0
## 246 0 0 1 0
## 247 0 0 0 0
## Kolor_Biały Kolor_Błękitny Kolor_Bordowy Kolor_Brązowy Kolor_Czarny
## 1 0 0 0 0 1
## 2 0 0 0 0 0
## 3 0 0 0 0 0
## 4 0 0 0 0 0
## 5 0 0 0 0 0
## 6 0 0 0 0 0
## 7 0 0 0 0 0
## 8 0 0 0 0 0
## 9 0 0 0 0 0
## 10 0 0 0 0 0
## 11 0 0 0 0 0
## 12 1 0 0 0 0
## 13 0 0 0 0 0
## 14 0 0 0 0 0
## 15 0 0 1 0 0
## 16 0 0 0 0 0
## 17 0 0 0 1 0
## 18 1 0 0 0 0
## 19 0 0 0 0 0
## 20 1 0 0 0 0
## 21 0 0 0 0 1
## 22 0 0 0 0 1
## 23 0 0 0 0 1
## 24 0 0 0 0 0
## 25 0 0 0 0 1
## 26 0 0 0 0 0
## 27 0 0 0 1 0
## 28 1 0 0 0 0
## 29 0 0 0 0 0
## 30 0 0 0 0 0
## 31 0 0 0 0 0
## 32 0 0 0 0 0
## 33 0 0 0 0 0
## 34 0 0 0 1 0
## 35 1 0 0 0 0
## 36 0 0 0 0 1
## 37 1 0 0 0 0
## 38 0 0 0 0 0
## 39 0 0 0 0 1
## 40 0 0 0 0 0
## 41 0 0 0 0 0
## 42 0 0 0 0 1
## 43 1 0 0 0 0
## 44 0 0 0 0 0
## 45 0 0 0 0 0
## 46 0 0 0 0 1
## 47 0 0 0 0 0
## 48 1 0 0 0 0
## 49 0 0 0 0 0
## 50 0 0 0 0 0
## 51 0 0 0 0 0
## 52 0 0 0 0 0
## 53 0 0 0 0 1
## 54 0 0 0 0 1
## 55 0 0 0 0 1
## 56 0 0 0 0 1
## 57 0 0 0 0 1
## 58 0 0 0 0 0
## 59 0 0 0 0 0
## 60 0 0 0 0 1
## 61 0 0 0 0 0
## 62 0 0 0 0 0
## 63 0 0 0 0 0
## 64 0 0 0 0 0
## 65 0 0 0 0 0
## 66 0 0 0 0 0
## 67 0 0 0 0 0
## 68 0 0 0 0 1
## 69 0 0 0 0 0
## 70 0 0 0 0 1
## 71 0 0 0 0 0
## 72 0 0 0 0 1
## 73 0 0 0 0 0
## 74 0 0 0 0 1
## 75 0 0 0 0 1
## 76 0 0 0 0 0
## 77 0 0 0 0 0
## 78 0 0 0 0 0
## 79 0 0 0 0 0
## 80 0 0 0 0 0
## 81 1 0 0 0 0
## 82 0 0 0 0 0
## 83 0 0 0 1 0
## 84 0 0 0 0 0
## 85 0 0 0 0 0
## 86 0 0 0 0 0
## 87 0 0 0 0 0
## 88 1 0 0 0 0
## 89 0 0 0 0 1
## 90 0 0 0 0 1
## 91 0 0 0 0 0
## 92 1 0 0 0 0
## 93 1 0 0 0 0
## 94 0 0 0 0 0
## 95 0 0 0 0 0
## 96 0 0 0 0 0
## 97 0 0 0 0 0
## 98 0 0 0 1 0
## 99 0 0 0 0 0
## 100 0 0 0 0 0
## 101 0 0 0 0 0
## 102 0 0 0 0 0
## 103 0 0 0 0 0
## 104 0 0 0 0 0
## 105 0 0 0 0 1
## 106 0 0 0 0 0
## 107 0 0 0 0 1
## 108 0 0 0 0 0
## 109 0 0 0 0 0
## 110 0 0 0 0 1
## 111 0 0 0 0 1
## 112 0 0 1 0 0
## 113 0 0 0 0 0
## 114 0 0 0 0 1
## 115 0 0 0 0 0
## 116 0 0 0 0 0
## 117 0 0 0 0 0
## 118 0 0 0 0 0
## 119 1 0 0 0 0
## 120 1 0 0 0 0
## 121 0 0 0 0 1
## 122 0 0 0 0 0
## 123 0 0 0 0 0
## 124 0 0 0 0 1
## 125 0 0 0 0 1
## 126 0 0 0 0 0
## 127 0 0 1 0 0
## 128 0 0 0 0 0
## 129 0 0 0 0 0
## 130 0 0 0 0 0
## 131 0 0 0 0 0
## 132 0 0 0 0 0
## 133 0 0 0 0 0
## 134 0 0 0 0 1
## 135 0 0 0 0 1
## 136 0 0 0 0 1
## 137 0 0 0 0 0
## 138 0 0 0 0 0
## 139 0 0 0 0 0
## 140 0 0 0 0 0
## 141 0 0 1 0 0
## 142 0 0 0 0 0
## 143 0 0 0 0 1
## 144 0 0 0 0 0
## 145 0 0 0 0 0
## 146 0 0 0 0 1
## 147 0 0 0 0 0
## 148 0 0 0 0 0
## 149 1 0 0 0 0
## 150 0 0 0 0 1
## 151 0 0 0 0 0
## 152 0 0 0 0 0
## 153 0 0 0 0 0
## 154 0 0 0 0 0
## 155 0 0 0 0 0
## 156 0 0 0 0 0
## 157 0 0 0 0 0
## 158 1 0 0 0 0
## 159 0 0 0 0 0
## 160 0 0 0 0 0
## 161 0 0 0 0 0
## 162 0 0 0 0 0
## 163 1 0 0 0 0
## 164 1 0 0 0 0
## 165 0 0 0 0 0
## 166 0 0 0 0 0
## 167 1 0 0 0 0
## 168 0 0 0 0 0
## 169 1 0 0 0 0
## 170 0 0 0 0 0
## 171 0 0 0 0 0
## 172 0 0 0 0 0
## 173 0 0 0 0 0
## 174 0 0 0 0 1
## 175 0 0 0 0 0
## 176 0 0 0 0 1
## 177 0 0 0 0 0
## 178 0 0 0 0 1
## 179 0 0 0 0 0
## 180 0 0 0 0 0
## 181 0 0 0 0 0
## 182 1 0 0 0 0
## 183 0 0 0 0 0
## 184 0 0 0 0 0
## 185 0 0 0 0 0
## 186 0 0 0 0 1
## 187 0 0 0 0 0
## 188 0 0 0 0 1
## 189 0 0 0 0 1
## 190 1 0 0 0 0
## 191 0 0 0 0 1
## 192 0 0 0 0 1
## 193 0 0 0 0 0
## 194 0 0 0 0 0
## 195 0 0 0 0 0
## 196 0 0 0 0 0
## 197 0 0 0 0 1
## 198 0 0 0 0 0
## 199 0 0 0 0 1
## 200 0 0 0 0 1
## 201 0 0 0 0 0
## 202 0 0 0 0 0
## 203 0 0 0 0 0
## 204 0 0 0 0 1
## 205 0 0 0 0 1
## 206 0 0 0 0 1
## 207 0 1 0 0 0
## 208 0 0 0 0 0
## 209 0 0 0 1 0
## 210 0 0 0 0 0
## 211 0 0 0 0 0
## 212 0 0 0 0 1
## 213 0 0 0 0 0
## 214 0 0 0 0 0
## 215 0 0 0 1 0
## 216 0 0 0 1 0
## 217 1 0 0 0 0
## 218 1 0 0 0 0
## 219 0 0 0 0 0
## 220 0 0 0 0 0
## 221 0 0 0 0 0
## 222 1 0 0 0 0
## 223 0 0 0 1 0
## 224 1 0 0 0 0
## 225 0 0 0 0 0
## 226 0 0 0 0 0
## 227 1 0 0 0 0
## 228 1 0 0 0 0
## 229 0 0 0 0 0
## 230 0 0 0 0 0
## 231 0 0 0 0 0
## 232 0 0 0 0 0
## 233 0 0 0 0 0
## 234 0 0 0 0 0
## 235 0 0 0 0 1
## 236 0 0 0 0 0
## 237 0 0 0 0 1
## 238 0 0 1 0 0
## 239 0 0 0 0 0
## 240 0 0 0 0 1
## 241 0 0 0 0 1
## 242 0 0 0 0 0
## 243 0 0 0 0 1
## 244 0 0 0 0 1
## 245 0 0 0 0 1
## 246 0 0 0 1 0
## 247 0 0 0 0 1
## Kolor_Czerwony Kolor_Fioletowy Kolor_Granatowy Kolor_Inny kolor
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 1
## 5 0 0 0 0
## 6 0 0 0 0
## 7 0 0 0 0
## 8 0 0 1 0
## 9 0 0 0 1
## 10 1 0 0 0
## 11 0 0 0 1
## 12 0 0 0 0
## 13 0 0 0 0
## 14 0 0 0 1
## 15 0 0 0 0
## 16 0 0 0 0
## 17 0 0 0 0
## 18 0 0 0 0
## 19 0 0 0 0
## 20 0 0 0 0
## 21 0 0 0 0
## 22 0 0 0 0
## 23 0 0 0 0
## 24 0 0 0 0
## 25 0 0 0 0
## 26 0 0 0 0
## 27 0 0 0 0
## 28 0 0 0 0
## 29 0 0 0 0
## 30 0 0 0 0
## 31 0 0 0 0
## 32 1 0 0 0
## 33 0 0 0 0
## 34 0 0 0 0
## 35 0 0 0 0
## 36 0 0 0 0
## 37 0 0 0 0
## 38 0 0 0 0
## 39 0 0 0 0
## 40 0 0 0 0
## 41 0 0 0 0
## 42 0 0 0 0
## 43 0 0 0 0
## 44 0 0 0 0
## 45 0 0 1 0
## 46 0 0 0 0
## 47 0 0 0 0
## 48 0 0 0 0
## 49 0 0 0 0
## 50 0 0 0 0
## 51 0 0 0 0
## 52 0 0 0 0
## 53 0 0 0 0
## 54 0 0 0 0
## 55 0 0 0 0
## 56 0 0 0 0
## 57 0 0 0 0
## 58 0 0 0 1
## 59 0 0 0 0
## 60 0 0 0 0
## 61 0 0 0 0
## 62 0 0 0 0
## 63 0 0 0 0
## 64 0 0 0 0
## 65 0 0 0 0
## 66 0 0 0 0
## 67 0 0 0 0
## 68 0 0 0 0
## 69 0 0 0 0
## 70 0 0 0 0
## 71 0 0 0 1
## 72 0 0 0 0
## 73 0 0 0 0
## 74 0 0 0 0
## 75 0 0 0 0
## 76 0 0 1 0
## 77 0 0 1 0
## 78 0 0 0 0
## 79 0 0 0 0
## 80 0 0 0 1
## 81 0 0 0 0
## 82 0 0 1 0
## 83 0 0 0 0
## 84 1 0 0 0
## 85 0 0 0 0
## 86 0 0 0 0
## 87 0 0 0 0
## 88 0 0 0 0
## 89 0 0 0 0
## 90 0 0 0 0
## 91 0 0 0 0
## 92 0 0 0 0
## 93 0 0 0 0
## 94 0 0 0 1
## 95 0 0 0 0
## 96 0 0 0 0
## 97 0 0 0 0
## 98 0 0 0 0
## 99 0 0 0 0
## 100 0 0 0 0
## 101 1 0 0 0
## 102 0 0 1 0
## 103 0 0 0 0
## 104 0 0 0 0
## 105 0 0 0 0
## 106 0 0 0 1
## 107 0 0 0 0
## 108 0 0 0 0
## 109 1 0 0 0
## 110 0 0 0 0
## 111 0 0 0 0
## 112 0 0 0 0
## 113 0 0 0 0
## 114 0 0 0 0
## 115 0 0 0 0
## 116 0 0 0 0
## 117 0 0 0 0
## 118 0 0 0 0
## 119 0 0 0 0
## 120 0 0 0 0
## 121 0 0 0 0
## 122 0 0 0 0
## 123 1 0 0 0
## 124 0 0 0 0
## 125 0 0 0 0
## 126 0 0 0 0
## 127 0 0 0 0
## 128 0 0 0 0
## 129 0 0 0 0
## 130 0 0 0 0
## 131 0 0 0 0
## 132 0 0 0 0
## 133 0 0 0 0
## 134 0 0 0 0
## 135 0 0 0 0
## 136 0 0 0 0
## 137 0 0 0 0
## 138 0 0 0 0
## 139 0 0 0 0
## 140 0 0 0 0
## 141 0 0 0 0
## 142 0 0 0 0
## 143 0 0 0 0
## 144 0 0 0 0
## 145 0 0 0 0
## 146 0 0 0 0
## 147 0 0 0 0
## 148 0 0 0 0
## 149 0 0 0 0
## 150 0 0 0 0
## 151 0 0 0 0
## 152 1 0 0 0
## 153 0 0 0 0
## 154 0 0 0 0
## 155 0 0 0 0
## 156 0 0 0 0
## 157 0 0 0 0
## 158 0 0 0 0
## 159 0 0 1 0
## 160 0 0 0 0
## 161 0 0 1 0
## 162 0 0 1 0
## 163 0 0 0 0
## 164 0 0 0 0
## 165 0 0 0 0
## 166 0 0 0 0
## 167 0 0 0 0
## 168 0 0 0 0
## 169 0 0 0 0
## 170 1 0 0 0
## 171 0 0 1 0
## 172 0 0 0 0
## 173 0 0 0 0
## 174 0 0 0 0
## 175 0 0 0 0
## 176 0 0 0 0
## 177 0 0 0 0
## 178 0 0 0 0
## 179 0 0 0 0
## 180 1 0 0 0
## 181 0 0 0 0
## 182 0 0 0 0
## 183 0 0 0 0
## 184 0 0 0 0
## 185 0 0 0 0
## 186 0 0 0 0
## 187 1 0 0 0
## 188 0 0 0 0
## 189 0 0 0 0
## 190 0 0 0 0
## 191 0 0 0 0
## 192 0 0 0 0
## 193 0 0 0 0
## 194 0 0 0 0
## 195 0 0 0 0
## 196 1 0 0 0
## 197 0 0 0 0
## 198 0 0 0 0
## 199 0 0 0 0
## 200 0 0 0 0
## 201 0 0 0 0
## 202 0 0 0 0
## 203 0 0 0 0
## 204 0 0 0 0
## 205 0 0 0 0
## 206 0 0 0 0
## 207 0 0 0 0
## 208 0 0 0 0
## 209 0 0 0 0
## 210 0 0 0 1
## 211 0 0 0 0
## 212 0 0 0 0
## 213 0 0 0 0
## 214 0 0 0 1
## 215 0 0 0 0
## 216 0 0 0 0
## 217 0 0 0 0
## 218 0 0 0 0
## 219 0 0 0 0
## 220 0 0 0 0
## 221 0 0 0 0
## 222 0 0 0 0
## 223 0 0 0 0
## 224 0 0 0 0
## 225 0 0 0 1
## 226 0 0 0 0
## 227 0 0 0 0
## 228 0 0 0 0
## 229 0 0 0 0
## 230 0 0 0 0
## 231 0 0 0 0
## 232 0 0 0 1
## 233 0 0 0 1
## 234 1 0 0 0
## 235 0 0 0 0
## 236 0 0 0 0
## 237 0 0 0 0
## 238 0 0 0 0
## 239 1 0 0 0
## 240 0 0 0 0
## 241 0 0 0 0
## 242 0 0 0 0
## 243 0 0 0 0
## 244 0 0 0 0
## 245 0 0 0 0
## 246 0 0 0 0
## 247 0 0 0 0
## Kolor_Niebieski Kolor_Pomarańczowy Kolor_Srebrny Kolor_Szary Kolor_Zielony
## 1 0 0 0 0 0
## 2 0 0 0 1 0
## 3 0 0 0 0 0
## 4 0 0 0 0 0
## 5 0 0 0 1 0
## 6 0 0 1 0 0
## 7 0 0 1 0 0
## 8 0 0 0 0 0
## 9 0 0 0 0 0
## 10 0 0 0 0 0
## 11 0 0 0 0 0
## 12 0 0 0 0 0
## 13 0 0 0 0 0
## 14 0 0 0 0 0
## 15 0 0 0 0 0
## 16 0 0 0 1 0
## 17 0 0 0 0 0
## 18 0 0 0 0 0
## 19 0 0 0 0 0
## 20 0 0 0 0 0
## 21 0 0 0 0 0
## 22 0 0 0 0 0
## 23 0 0 0 0 0
## 24 1 0 0 0 0
## 25 0 0 0 0 0
## 26 0 0 0 1 0
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## [ reached 'max' / getOption("max.print") -- omitted 1001 rows ]autka_dt_MM_no_power <- subset(autka_dt_MM,select = -4)
autka_dt_MM_no_power## Rok_produkcji Przebieg Pojemność_skokowa Cena Marka_Model_Alfa Romeo 159
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## Marka_Model_Alfa Romeo Giulia Marka_Model_Alfa Romeo Giulietta
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